Proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS)
O Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS) – antigo Workshop de Informática Médica (WIM) – é o evento científico anual da Comissão Especial de Computação Aplicada à Saúde (CE-CAS) da Sociedade Brasileira de Computação, que tem como objetivo reunir pesquisadores, estudantes, professores, profissionais e empresários interessados em aplicações da Computação na área de Saúde. As atividades no SBCAS compreendem a apresentação de artigos resultantes de pesquisas da área, apresentação de trabalhos em andamento, além de palestras convidadas e mesas de debate sobre temas e desafios atuais.
Sociedade Brasileira de Computação - SBC
A Sequential Model for Identifying Cognitive Impairment Using Accelerometer Data
The progression of certain cognitive impairments can be slowed when identified early. However, current diagnostic methods are costly and not widely accessible to the general population. This paper presents a sequence model that leverages accelerometer data to characterize behavioural routines, enabling the differentiation between cognitively impaired and mentally healthy individuals. The raw data, collected at 80 Hz, was consolidated into 24-hours time windows and represented by 13 features. The final dataset (N = 2934) with 429,912 hours of data was used to train a Conv1D-based sequence model, which achieved significantly higher AUC-ROC (0.90 ± 0.01) than cross-sectional models (0.59 ± 0.02).
Enhancing HAR Novelty Detection with Activity Confusion Analysis and Clustering
The field of medicine and healthcare, especially Human Activity Recognition (HAR), has been experiencing the benefits of technology for tracking people’s habits. In this context, novelty detection aims to detect if an activity performed by a subject hasn’t been seen before, in the past training data. However, the novelty may not be properly recognized if there is a similar action in the train set. Thus, this work proposes an analysis of activity pairs to determine which pairs are most confused when using three traditional models for novelty detection: Local Outlier Factor, One-class SVM, and Isolation Forest. After that, we employ agglomerative clustering to gather similar activities, and the clustered activities are used as input to a leave-one-activity-out novelty detection approach. We conclude that the Isolation Forest model achieves the best results in the activity pairs analysis, yielding an F1 score of 88.4%. Finally, the leave-one-activity-out methodology is evaluated with a sliding window technique, and the F1 score on the Local Outlier Factor model increases from 66.3% (without grouping similar activities) to 74.6% (grouping similar activities).
Estimating Hospital Bed Occupancy for COVID-19 Treatment Using Temporal Data
Efficient management of the quantity and types of hospital beds is a critical challenge in public healthcare, as highlighted during the COVID-19 pandemic. This study proposes an approach based on artificial neural networks of the Multi-Layer Perceptron (MLP) type and time series analysis, using data collected in the state of Mato Grosso between 2020 and 2022. The model was trained with hospitalization data from the previous two weeks to predict the demand for clinical and complementary beds, achieving a root mean square error (RMSE) of 45.83 for a two-week forecast horizon. The results demonstrate its applicability as a decision-support tool for healthcare managers, contributing to a more efficient allocation of hospital resources.
Classification of Alzheimer’s Disease by Magnetic Resonance Imaging with an Ensemble Approach
Alzheimer’s Disease (AD), affecting over 55 million people globally, demands reliable diagnostic tools. Single-model approaches using CNNs and traditional ML face critical limitations. This study proposes two frameworks: a stacking-CNN ensemble (VGG-16, ResNet-101, DenseNet-121) and two voting ML ensembles (Voting[all]: KNN, RF, SVC, LR, XGBoost; Voting[few]: KNN, RF, XGBoost). Evaluated on 6,400 MRIs, Voting[few] achieved the highest classification metrics (97.8% accuracy; 0.984 MCC; 93.8% F1macro), outperforming individual CNNs, validated through Friedman-Nemenyi tests. Results suggest, in this context, that simpler ML models might better capture the inherent characteristics of MRI data for AD diagnosis.
A-Track: An Ontological Approach to Assisting Anxiety Management
Anxiety, a natural survival mechanism, becomes chronic under modern stressors, escalating into chronic disorders with multifaceted health impacts. While early detection is crucial, healthcare systems struggle with scalability. This study introduces the A-Track Ontology, a digital tool designed to model anxiety through personalized context histories. Validated through logical consistency, domain coverage, and utility assessments, the ontology synthesizes multimodal data into actionable insights for proactive intervention. Integrating ontological reasoning with real-world context awareness, this approach addresses clinical scalability gaps, enabling personalized, data-driven strategies for anxiety management.
An Intelligent Model to Support People with Problematic Smartphone Use
Problematic Smartphone Use (PSU) is characterized by compulsive usage patterns that can negatively impact routines and well-being. This paper introduces the Kratos model, which leverages context awareness, context histories, machine learning, and ontological inferences to detect PSU and recommend personalized interventions. The model was evaluated using simulated data from 49 users over 30 days, demonstrating the model’s effectiveness in identifying and mitigating problematic use. Kratos enables the detection of risk patterns and the recommendation of tailored interventions, complementing traditional methods that rely on questionnaires and manual interventions.
Endometrioid Ovarian Carcinoma Classification through Color Scheme Transformation and Radiomics in Histopathological Images
Endometrioid ovarian carcinoma (EOC) is a subtype of ovarian cancer with a challenging diagnosis in histopathological images. Convolutional Neural Networks (CNNs) are widely used for this task but require large amounts of data and high computational burden. This work proposes an approach based on color scheme transformation, Radiomics, and incremental feature selection (I-RFE), applied to multiple classifiers. The experiments showed that the proposed method outperforms CNNs in the literature, achieving an accuracy of 97.96% and an F1-Score of 98.26%, while the best CNN reached 75% accuracy. These findings suggest that the approach can improve EOC detection, making it a promising alternative for diagnostic support.
MoveBoost: A model to stimulate motor control development in children through gamification in school environments
Motor development is essential for children’s growth, but its stimulation in schools is often limited. MoveBoost complements a game called GameMove that stimulates children’s motor control, using gamification and exergames to promote motor skills such as balance and coordination. The application incorporates rankings, achievements, rewards, and goals to engage children in a playful way. In addition, it analyzes data on users’ actions and proposes personalized challenges to stimulate progress. A usability evaluation was conducted using the TAM model, involving GameMove experts, and indicated high levels of ease of use (92%) and perceived usefulness (88%).
A Computational Model for Depression Analysis in Social Media Data
Mental health disorders affect millions, compromise quality of life, and reduce productivity. Depression impacts around 5% of adults worldwide and costs nearly US$1 trillion annually. We propose Serapis, a computational model that organizes data from social networks, questionnaires, and the PHQ-9 to produce actionable intelligence for depression diagnosis. Our model integrates LLMs with an ontology built on best practices in forensic computing and OSINT. We developed an MVP prototype that builds a timeline of posts and highlights entries relevant to psychologists. A focus group with professionals validated the system’s usability, reporting high ease of use and perceived utility, and recommended improvements in contextual accuracy and collaboration features.
Atlas-Guided U-Net++ with EfficientNetB5 for Automatic Pancreas Segmentation in Abdominal CT Scans
Pancreas segmentation in abdominal computed tomography images is challenging due to the organ’s variability in shape, size, and position. This work proposes an automatic segmentation method based on a 2D Convolutional Neural Network (CNN) approach, consisting of three steps: (1) filtering non-pancreas slices using a CNN, (2) region of interest detection via a probabilistic atlas, and (3) final segmentation with U-Net++ with an EfficientNetB5 backbone. The method achieves a mean Dice coefficient of 78.59% and Recall of 79.12%, with a lower computational cost compared to 2.5 and 3D approaches. Thus, our results stand out among state-of-the-art methods, providing a computationally efficient and accurate solution for diagnosis and treatment planning.
Coronary Artery Disease Classification Using Deep Neural Networks
Coronary artery disease (CAD) is one of the leading causes of global mortality, highlighting the need for more effective early diagnosis methods. This study compares LSTM, Bi-GRU, and Transformer neural networks in classifying electrocardiograms (ECG) into three categories: normal, inferior myocardial infarction (IMI), and anterior myocardial infarction (AMI), using the PTB-XL dataset. Results show that Transformers offer faster training (average of 35 minutes), while LSTM and Bi-GRU achieve higher accuracy (91% and 92%, respectively). The study also contributes to improving ECG data preprocessing and training for cardiac diagnosis classification.
Nuclei Segmentation in Cervical Cytology Images Using Pix2Pix and U-Net
The segmentation of cell nuclei is essential for the analysis of cytological images and the early detection of diseases such as cervical cancer. This work compares the U-Net and Pix2Pix architectures in the task of semantic segmentation of cervical cell nuclei. Pix2Pix is a conditional GAN that uses adversarial learning. The experiments were conducted using the CNSeg dataset, which contains cytological images in various contexts. U-Net achieved better performance in AJI (+2.90%) and PQ (+3.12%) metrics. However, Pix2Pix showed promising results by producing accurate segmentations. These findings suggest that adversarial networks can be a promising alternative for this task, which remains underexplored in the literature.
Investigating the Use of Federated Learning for Diabetes Detection and Management
This study explores federated learning for diabetes detection and monitoring using physiological data from wearable-like devices. A server and two ESP32 devices form the system, aiming to enhance early diagnosis and personalized care. Three datasets—Ohio T1DM, DiaHealth, and GBS—were processed with dimensionality reduction, outlier removal, and balancing techniques. Key biomarkers include glycemic index, heart rate, temperature, sweat, and oxygen saturation. Machine learning models were trained in Orange Data Mining and evaluated via precision, recall, F1-score, and accuracy. Random Forest excelled as the global model within the federated framework, with local customization on ESP32 despite memory constraints. Federated learning proves promising for personalized diabetes monitoring, with future improvements suggested through daily activity data integration.
AI-assisted medical image annotation: a study on skin lesion segmentation by non-experts
Image annotation is essential for building databases for training artificial intelligence (AI) algorithms. However, the dependence on specialists makes this process expensive and difficult to scale. This work proposes an interactive framework to assist non-specialists in segmenting skin lesions. Its effectiveness and usability were evaluated in an experiment with 50 volunteers, who segmented skin lesions in two modes: manual and assisted by the tool. The results indicate that the assisted approach improves efficiency without compromising accuracy, especially when combined with crowdsourcing. The proposed tool is available in open-source format and can be used in different medical areas beyond dermatology.
OntoDemer: An Ontology for Identifying Disease Risks Based on Wheelchair User Behavior
The global increase in disabilities highlights the need to manage health behaviors, especially for wheelchair users, who face unique challenges. Advances in Human-Computer Interaction (HCI), particularly in health monitoring, are effective tools for improving self-care. This article presents OntoDemer, an ontology designed to identify behaviors and risk factors in wheelchair users, serving as a foundation for disease prevention. The study identifies a lack of specific ontologies in this area. The entities, metrics, and relationships of OntoDemer demonstrated its ability to detect health risks. Evaluations using competency questions confirmed its usefulness, supporting preventive recommendations.
Lightweight Method for Yoga Posture Recognition: Contributions to Well-Being and Quality of Life
Recognizing the growing importance of yoga for enhancing physical health and mental well-being, this paper proposes a lightweight neural network method for the automatic recognition of yoga postures from images. By leveraging skeletal keypoints, our model achieves efficient and accurate posture classification. We evaluated our approach on the Yoga-82 dataset using two data augmentation strategies: horizontal flipping of images and data balancing via random Gaussian noise addition combined with keypoint fusion. Our model attains an accuracy of 90.31% with only 85,582 parameters, demonstrating competitive performance relative to more resource-intensive methods. This efficiency makes the approach particularly suitable for resource-constrained environments, such as smartphones, and paves the way for developing tutor applications that promote individual yoga practice and enhance overall well-being.
Vision Transformers with Dynamic Patches for Histological Slide Analysis
This work presents an approach to improve Vision Transformers (ViT) by implementing dynamic patch input. Experiments were conducted with different types of models, performing fine-tuning and exploring multiple strategies to identify the most relevant issues related to patch extraction. The proposed approach was compared to the traditional ViT model in a study using the Cell Recognition and Inspection Center (CRIC) dataset, composed of Pap smear images. The results demonstrated that the fine-tuning of the ViT-Small model with Grid patch extraction achieved an accuracy of 0.81 while the best dynamic approach obtained 0.78 of accuracy, due to the excessive overlapping of patches.
Human Digital Twins for Personalized Healthcare Training: An Evidence-Based Organizational Proposal
The training of healthcare professionals faces challenges such as lack of personalization. This study proposes a Human Digital Twin (HDT) model structured into two dimensions: individual and professional relationships, enabling continuous learning personalization. The model, developed based on a case study, integrates a data pipeline to capture organizational, physiological, and performance-related information, adapting recommendations to the professional’s profile. A scenario-based evaluation highlights the HDT’s potential to optimize training and support professional development throughout the career. This proposal extends HDTs to the context of healthcare training.
Analyzing the Trade-off Between Fairness and Model Performance in Supervised Learning: A Case Study in the MIMIC dataset
Fairness has become a key area in machine learning (ML), aiming to ensure equitable outcomes across demographic groups and mitigate biases. This study examines fairness in healthcare using the MIMIC III dataset, comparing traditional and fair ML approaches in pre, in, and post-processing stages. Methods include Correlation Remover and Adversarial Learning from Fairlearn, and Equalized Odds Post-processing from AI Fairness 360. We evaluate performance (accuracy, F1-score) alongside fairness metrics (equal opportunity, equalized odds) considering different sensible attributes. Notably, Equalized Odds Post-processing improved fairness with less performance loss, highlighting the trade-off between fairness and predictive accuracy in healthcare models.
Critical analysis of the performance of the YOLO architecture in the detection of oral lesions from clinical images
The objective of this work was to train a convolutional neural network for the detection of oral cavity lesions in heterogeneous clinical images, using YOLOv5, as well as the critical analysis of its effectiveness. The database had four categories of elementary oral lesions, without stipulating protocols for obtaining the images, such as distance, angle, and illumination. YOLO showed the best performance in detecting vesicular/blister lesions in both models analyzed: YOLOv5m mAP@50 was 86.8% and in the YOLOv5x model it was 80.7%, followed by papule/nodule in both tests. Images containing only one lesion showed better performance. We considered the quality of the detections obtained to be satisfactory in the majority of images despite using a small dataset for this evaluation.
An Explainable Classifier for Post-Traumatic Stress Disorder Using 3D Convolutional Neural Networks
O Transtorno de Estresse Pós-Traumático (TEPT) é uma condição psiquiátrica caracterizada por sintomas persistentes de reexperiência, evitação e hiperexcitabilidade em resposta a eventos traumáticos. A identificação precisa de indivíduos com TEPT a partir de dados neurobiológicos permanece um desafio, motivando o uso de abordagens baseadas em aprendizado profundo. Neste estudo, empregamos redes neurais convolucionais tridimensionais (3D-CNNs) para a classificação de TEPT a partir de dados de ressonância magnética funcional (fMRI). Utilizamos uma amostra de 43 participantes (20 com TEPT) expostos a estímulos visuais aversivos e avaliamos o desempenho do modelo por meio de validação cruzada, obtendo uma acurácia média de 86,25%. Além disso, empregamos a técnica de oclusão, baseada no Atlas Harvard-Oxford, para identificar as regiões cerebrais mais relevantes para a classificação. Os resultados destacam o envolvimento de áreas associadas ao processamento visual e emocional, incluindo o giro fusiforme occipital, a divisão superior do córtex occipital lateral e o córtex pré-cúneo.
Cardiac pathology classification with one-dimensional convolutional neural network
Currently, technology is indispensable in the medical field and the use of artificial intelligence tools is responsible for accelerating several processes, facilitating data acquisition, and recognizing important patterns for patient diagnosis. Focusing on cardiac pathologies, the electrocardiogram is the most widely used examination to diagnose patients, and optimizing this process is extremely important. Therefore, the objective of this study is to develop a model capable of classifying cardiac pathologies using raw ECG signals. For this purpose, signals from the PTB-XL database, recorded in the 12-lead standard, were used to train a 1D convolutional neural network without pre-training. Various hyperparameters were adjusted to find the model that is the best suited to the application. The model was evaluated using a test dataset, achieving an accuracy of 84%. The model demonstrated satisfactory performance, which can lead to the possibility of improving the current diagnostic system by accelerating the examination reading process and helping healthcare professionals interpret the results.
An approach based on IoT and machine learning for monitoring patients on healthcare centers
Internet of Things (IoT) enables remote monitoring using existing networks, integrating diverse computing systems and communication protocols. This adaptability makes IoT applicable across domains, including healthcare. Hospitals leverage the Internet of Medical Things (IoMT) to collect and transmit patient data, enabling real-time diagnoses, operational efficiency, and personalized care. This paper presents a patient monitoring system for healthcare centers using IoMT and machine learning (ML). The system is capable of performing facial recognition of patients, identifying movement patterns associated with falls, using cameras, and sending alerts to caregivers or healthcare professionals. Results demonstrate the system’s feasibility in enhancing healthcare outcomes. The system achieved a precision of 95% for facial recognition and 97% for fall detection.
A LLM-Based Approach for Analyzing Vaccine Misinformation
This article proposes a fully LLM-based approach to extract insights from large-scale data on vaccine misinformation. Using BERT and GPT models, we examined millions of anti-vaccine messages from Telegram. The analysis identified 12 key misinformation narratives, highlighting alarmist concerns and conspiracy theories, while also suggesting mitigation strategies. The results demonstrate the potential of LLMs for large-scale automated misinformation analysis, mapping its dynamics and providing insights for the development of agile and evidence-based strategic responses.
Challenges and Innovations in Healthcare Fraud and Waste Detection Systems: A Systematic Review and Proposed Framework
Fraud and abuse in the healthcare sector cause considerable financial losses to public and private entities.This article conducted a systematic literature review to identify the challenges in fraud and waste detection systems. Ten articles were identified in the review that covered the IEEE, PubMed, Scopus, and Web of Science bases. Based on these studies, we developed a framework in which we categorized the works by anomaly point, contextual, and collective. We also identified the unsupervised learning techniques used and identified the following challenges dimensionality reduction, conceptual deviations caused by changes in fraudsters’ behavior to bypass audit systems, and the need for real-time detection support.
Evaluating Language Models' capacity for sentiment analysis in mental health contexts
Mental disorders negatively impact peoples’ quality of life. These conditions manifest through sentiments such as sadness, loneliness, apathy, and fear. Thus, sentiment analysis can enhance the early identification of mental symptoms. Therefore, this article evaluates the capacity of language models based on Artificial Intelligence concerning the sentiment analysis of texts in Brazilian Portuguese. The evaluation process occurred based on a dataset of 5,000 text posts from Twitter. The caramelo-smile-2 model obtained the best performance in the experiment, reaching approximately 0.8 of precision, recall, and f1-score. The results highlight the potential of using pre-trained language models for sentiment analysis of Brazilian Portuguese texts, which can allow the utilization of these models in applications to analyze text messages of patients diagnosed with mental disorders.
MarIA - DeepSeek: A Language Model-Based Assistant Proposal for Community Health Agents
The first 1,000 days of life, including pregnancy and the child’s first two years, represent a critical window for preventing non-communicable chronic diseases (NCDs). However, Community Health Agents (CHAs) face challenges in accessing and applying evidence-based recommendations during this period. This work presents MarIA DeepSeek, a virtual assistant powered by large language models (LLMs), which integrates techniques such as Prompt Chaining, Retrieval-Augmented Generation (RAG), and expert-curated documents to deliver personalized, evidence-based guidance to CHAs. Experiments showed that the tool improves the accuracy, clarity, and accessibility of recommendations, outperforming general-purpose models like GPT-4.0 and Gemini, and enhancing decision-making in primary health care.
Facial Image Analysis for Parkinson’s Identification in Low-Data Contexts
This study investigates the use of 2D facial images to identify Parkinson’s Disease (PD) in data-limited scenarios. The exploration of facial expressions in 2D images remains scarce in the literature. To help bridge this gap, a complete pipeline was implemented. Predefined facial feature extraction approaches were compared with automatic extraction using pre-trained convolutional neural network models. Among the evaluated models, the combination of DenseNet-201 for feature extraction and SVM for classification achieved the best performance, reaching 94.75% accuracy. The results reinforce the potential of 2D facial image analysis for PD identification, despite challenges related to data scarcity.
Comparing YOLO and Detectron2 models for automatic extracting patients information from leprosy assessment form
Leprosy, caused by Mycobacterium leprae, remains a global challenge, requiring strategies to achieve disease elimination by 2030. In Brazil, the Simplified Neurological Assessment (from Portuguese Avaliação Neurológica Simplificada, ANS) is mandatory for suspected cases; however, the form is still manually fulfilled, which limits the use of data. This study evaluates computer vision models (YOLOv8x, YOLO11x, Faster R-CNN) for detecting hand and foot sensitivity regions from ANS forms. All models were evaluated based on precision, recall, mean average precision (mAP) and confusion matrix. YOLO variants achieved over 94% precision and 84% recall across all classes. Automating ANS data extraction can facilitate the creation of structured datasets, enhancing disease monitoring and enabling the train of predictive models.
Bias Propagation in Health AI: Measuring Pre-Training Bias and Its Effect on Machine Learning Model Outcomes
Machine learning (ML) has become an essential tool in healthcare, supporting diagnosis, prognosis, and treatment decisions. However, biases present in pre-training data can compromise both model performance and fairness, disproportionately affecting underrepresented groups. This study systematically examines the impact of four pre-training bias metrics on the accuracy of three ML models across four health-related datasets. Our findings show that more data does not necessarily translate to better performance, particularly when data imbalance and bias are present. Moreover, pre-training bias metrics are associated with accuracy disparities, underscoring the importance of proactive bias assessment to develop more equitable ML models in healthcare.
Forecasting Weekly Dengue Cases in Brazilian Federative Units
Effective epidemic management requires proactive measures, making the accurate prediction of weekly dengue cases in specific regions essential for prevention and control strategies. In this study, we evaluated the effectiveness of classical statistical techniques and machine learning methods in forecasting the number of weekly dengue cases across 27 Brazilian federative units. For each unit, we explored a multivariate one-step prediction strategy, applying wavelet filtering to the features to enhance signal decomposition and improve predictive performance. We investigated the univariate LightGBM model trained on data from 26 cities (cross-learning) and validated it individually in each federative unit using the univariate leave-one-out (LOO) technique with one-step predictions. Additionally, we demonstrated the model’s generalizability by training LightGBM on data from all Brazilian federative units and validating it in a different geographic location, San Juan. The LightGBM LOO model exhibited superior generalization compared to other shallow, deep, and foundation models, including TimeGPT-1 and MOIRAI.
Anonymization of Clinical Texts Using LLMs
Data-driven model training is essential for healthcare advancements, enabling more personalized medicine. Clinical text anonymization protects patient privacy amid increasing digitalization. Traditional methods, while effective, may reduce data utility and fail in contextual anonymization. This study proposes a method based on large language models (LLMs), combining named entity recognition (NER) and text rephrasing to ensure coherence and anonymization. Tested on therapeutic transcripts, the method achieved high accuracy in removing sensitive information while preserving textual integrity, making it applicable across contexts.
Uncovering Potential Proteomic Biomarkers for Cancer Patients with COVID-19 Infection using Multilabel Deep Learning Model
The effects of COVID-19 on cancer patients are concerning. This work proposes a framework that employs a multilabel classifier processing longitudinal proteomics patients’ data to identify potential proteomic biomarkers that correlate cancer and COVID-19. The framework uses Olink NPX data from 305 COVID-19-positive cancer patients. Stratified k-fold cross-validation addresses data imbalance. The overall average results show a Jaccard index of 88.79%, a hamming loss of 0.32%, a Wasserstein distance of 0.64%, and an area under the curve of 94.47%, across 312 labels, with four proteins presenting a Jaccard index of 97% or above, identified as proeminent biomarkers.
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.1
Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.
A Deep Features-Based Approach for Serous Ovarian Cancer Diagnosis in Histopathological Images
Serous ovarian carcinoma (SOC) is an aggressive subtype of ovarian cancer with a challenging diagnosis in histopathological images. This work proposes an approach to define the optimal color scheme for image representation and extract Deep Features. The experiments demonstrated that the proposed method outperforms other Convolutional Neural Network (CNN)-based approaches, achieving 94.99% accuracy. Additionally, it proved effective in differentiating SOC from other subtypes, such as clear cell, endometrioid, and mucinous carcinomas. These results highlight the potential of the proposed approach to assist in SOC diagnosis, providing a promising tool for clinical decision support.
Evaluating the quality of health apps usin uMARS methodology and personas
Mobile health applications have become popular tools for assisting users with self-care. However, there is limited evidence regarding their quality. This study evaluates free health applications using the User Mobile App Rating Scale (uMARS) methodology. By comparing app characteristics with the expectations of typical users, the research identifies factors influencing the adoption and retention of these solutions. The results reveal significant variability in app quality, particularly in usability, credibility, and alignment with individual user needs. These findings underscore the need for better alignment between health apps and user demands.
Autoencoders to detect manifestation shift in medical images
Advanced intelligent models can potentially assist medical professionals with their routine tasks. Nonetheless, a recognized challenge is that these algorithms often excel in operating in-distribution settings but may underperform when confronted with Out-of-Distribution situations from diverse sources, mainly caused by manifestation shifts. This inconsistency underscores the disparity between lab-based findings and real-world clinical applications, necessitating reliable methods for measuring uncertainty. This study introduces a novel approach proposing self-awareness in detecting manifestation shifts within medical images, designing the challenge as a classification of lesion subtypes. The proposed method achieved competitive performance compared with recent literature utilizing reconstruction thresholds based on histogram analysis. Furthermore, a review of erroneous predictions uncovered various confounding factors, contributing to a better understanding of the models’ limitations.
AllianceScan: identification of disfluencies in brazilian portuguese in psychotherapy audio transcripts to predict therapeutic alliance
Speech disfluencies, such as pauses and repetitions, can indicate both cognitive processes and underlying emotional states. This study proposes AllianceScan, an approach to identify disfluencies in transcriptions of Brazilian Portuguese audio, applied in psychotherapy sessions. Using speech recognition, the approach classifies four types of disfluencies and employs regression models to predict therapeutic alliance. The results, evaluated by metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Median Absolute Error (MDAE), indicate that the approach can enhance the understanding of therapeutic interactions and identify signs of treatment progress.
Glaucoma Diagnosis in Retinal Images from Portable Ophthalmoscope Using Transformer-Based Ensembles
This paper explores the use of Transformers-based models for the detection of glaucoma in portable ophthalmoscope retinograms, addressing the growing need for accessible diagnostics in regions with limited resources. Glaucoma is a chronic eye disease that can lead to irreversible blindness if not diagnosed early, and the implementation of automatic methodologies to aid screening is essential. The research uses a dataset made up of 2,000 fundus images, captured under practical conditions and with reduced quality, to train and evaluate the models. The results show that Transformers models, such as SwinV2, BEiT, DeiT and ViT, perform competitively compared to previous approaches based on convolutional networks, highlighting the potential of these models in classifying glaucoma from low-resolution retinograms. Combining the models’ predictions in a Ensemble resulted in an average accuracy of 93.25%, demonstrating the effectiveness of the proposed approach.
Hierarchical Classification of Breast Cancer Cells Using MIBI Images and Machine Learning
Breast cancer is one of the leading causes of mortality among women worldwide, with diagnosis often occurring too late. Early detection is crucial to prevent advanced stages and improve prognosis. Aggressive subtypes, like triple-negative breast cancer (TNBC), require personalized therapies based on tumor profiling. Analyzing the tumor microenvironment is challenging due to the complexity of identifying tissue components. Advances in computer vision and deep learning have enabled image segmentation and classification methods. This study proposes a method for classifying cells by general type and immune identity. The approach was tested on TNBC images and showed promising AUC results for classifying cells into general (0.9998) and immune (0.9994) types.
Prediction of Gold Standard Gait Data from Inertial Data: A Machine Learning Approach
Biomechanics laboratories typically use kinematic cameras and force platforms as the gold standard for gait assessment. However, these systems are expensive and have limited availability. Wearable devices, equipped with sensors—primarily inertial sensors—can capture movement data, enabling the inference of human gait behavior. To enhance the quality of measurements obtained from wearables, this study investigates the feasibility of predicting kinematic parameters from inertial data collected by wearable sensors. Machine learning techniques, including Random Forest, XGBoost, and Gradient Boosting, were used to correlate inertial measurements with data from traditional motion capture systems. Feature importance analysis and SHAP highlighted the significance of velocity and acceleration in predicting kinematic parameters. Experimental results indicate that tree-based models, particularly Gradient Boosting and XGBoost, achieved the best performance, with coefficient of determination values close to 0.989, demonstrating the feasibility of the proposed approach.
Vascular Bifurcation-Based Image Stitching Applied to Low-Resolution Retinal Images
Image stitching is a technique that allows combining multiple images, forming a single image with a wide field of view. In the context of retinography, this technique is crucial to capture a detailed view of the retina, allowing broader examinations to be performed. In this work, we present a method for stitching low-resolution images, using bifurcation points as image features. The proposed method presents an increase in the correspondences obtained in relation to the detectors in the literature, obtaining the RMSE result with a reduction of approximately 14% compared to SIFT (24.29) and ORB (24.32). Furthermore, the proposed method obtained a higher average PSNR, reaching 26.27 for glaucoma images, 26.72 for normal images and 26.71 for images of suspected patients, while the methods based on SIFT and ORB presented lower values, confirming the effectiveness of the proposed approach.
Classification of Cancerous Cervical Cells Using CNNs and ViTs in Ensemble for Medical Diagnosis Support
Cervical cancer is a global public health issue that requires effective screening methods. This work proposes an ensemble of EfficientViT, EVA-02, and EdgeNeXt for the automatic classification of cervical cells. The Herlev (917 images) and SIPaKMeD (4049 images) datasets were evaluated for binary and multiclass classification tasks. The methodology includes transfer learning, data augmentation, and 5-fold cross-validation. In the Herlev dataset, the results were 98.35% accuracy (binary) and 83.40% (7 classes). In SIPaKMeD, the model achieved 99.73% (binary), 98.96% (3 classes), and 98.08% (5 classes), reaching state-of-the-art performance. The results demonstrate the model’s potential to assist in medical diagnosis, reducing the need for manual analysis.
Discovery of Conditionally Independent Networks Among Gene Expressions in Breast Cancer Using Fast Step Graph
The heterogeneity of the causes of breast cancer and these complex gene interactions that characterize this neoplasm present significant challenges to understanding and treating the disease. This study is motivated by the need to identify interconnected networks of breast cancer genes, specifically those that represent conditional independence relationships. To construct these networks, we propose the use of the Fast Step Graph algorithm, which belongs to the family of sparse, high-dimensional Gaussian Graphical Models, applied to the PAM50 gene expression dataset. This dataset was stratified according to estrogen and progesterone receptors, key elements for prognosis and personalized therapy. The application of the algorithm resulted in four graphs highlighting the relationships among the genes involved in breast cancer. These findings support the hypothesis that there are specific gene sub-networks and contribute to a deeper understanding of gene interactions in this cancer, potentially offering new insights for future research and new therapeutic strategies.
Reducing Intra-Subject Bias in Parkinson’s Disease Voice Classification with Ant Colony Optimization
Parkinson’s Disease (PD) is a neurodegenerative condition that affects voice production. Previous studies have employed machine learning techniques for PD diagnosis based on voice data. However, the vast majority of existing approaches overlook intra-subject variation in their methodologies. This study proposes a methodology that accounts for such variation while also employing the bio-inspired Ant Colony Optimization algorithm for feature selection, thereby reducing overfitting. An accuracy of 84.80% was achieved in distinguishing individuals with PD from healthy controls, providing a fairer and reliable approach and highlighting the potential of bio-inspired algorithms in PD diagnosis.
Exploring everyday objects as tactile guides for medical simulation in virtual reality
Virtual reality environments play an important role in the health education process, providing safe environments for professional practice. The tactile sensation and the forces associated with movements are fundamental factors for training medical procedures that involve psychomotor skills, such as surgeries, sutures and invasive examinations. In virtual reality systems, these sensations are provided by haptic devices, which offer limitations in cost and functionality. Aiming to propose an alternative to these devices, this study investigates the impact of tactile guides on interaction in virtual reality environments for medical training. In this work, was investigated how the weight, texture, shape and grip of conventional objects, used as tactile guides of a scalpel in a virtual reality environment, impacted the user experience. The results indicated that familiar and ergonomically balanced objects result in more comfort and immersion, while inadequate shapes compromise the experience.
Intelligent Analysis of Archimedean Spirals for Parkinson’s Disease Indicator Identification
Parkinson’s disease is a neurological condition that impairs movement, often associated with reduced dopamine levels in the brain. The Archimedean spiral drawing, a sophisticated motor task requiring coordination, provides an accurate assessment of motor function. This research presents an innovative methodology that preprocesses images by dividing them into segments, stacking them, and summing the pixels of each to generate another image. With promising results, achieving 95.4% accuracy and an F1-Score of 97%, this approach could be a valuable tool to support specialists in diagnosing the disease.
Brain Signal Analysis for Attention Level Detection in Digital Games
This study proposes the use of brain signals for detection and analysis of focus of attention in 4 games using mouse and touchpad. The approach used uses the Bitalino device to capture brain waves and Wavelet decompositions and spectrogram analysis to detect different levels of attention. Results identified a mean baseline reference of 55% and indicated that games without a time limit helped to increase the attention of the participants over time, while others had difficulty maintaining focus, regardless of the device used. The findings highlight the possibility of designing interactive technologies that foster greater engagement and consider the natural dynamics of human attention.
Suicide Trend Analysis in Brazil: The Effect of the COVID-19 Pandemic
Suicide is one of the leading causes of death worldwide. Linked to the mental health conditions of a population, stressful global events can affect trends. This study analyzed suicide trends in Brazil between 2003 and 2022, focusing on the COVID-19 pandemic period. We used data from the Mortality Information System (SIM) of DATASUS (218,707 suicide occurrences). Suicide mortality rates were predicted for the pandemic years and beyond (2020-2022) using time series models (Prophet, SARIMA, and LSTM) trained with data up to 2019. Here, we compare the actual results with those forecasted to evaluate a possible impact of the pandemic. Our main findings show that the suicide rate in Brazil nearly doubled over two decades (from 4.45 to 8.11 per 100,000 inhabitants), with a pronounced increase during the COVID-19 pandemic. The time series models tested here performed well prior to 2020 but underestimated suicide peaks during the pandemic, underestimating the impact of COVID-19 on forecasting complex phenomena such as suicide. Our analysis highlighted the importance of public policies focused on mental health and reinforces the need for preventive actions, especially in the context of crises such as COVID-19.
Automatic Detection of Macrophages in Canine Bone Marrow Smears Using Deep Learning
Canine visceral leishmaniasis (CVL) is a neglected zoonosis affecting dogs and humans, posing significant public health challenges due to diagnostic difficulties. This study aimed to enhance the detection of macrophages in microscopic images of canine bone marrow aspirates by employing deep learning techniques. The YOLOv8 model was utilized in conjunction with data augmentation strategies, including color adjustments and geometric transformations. The results were promising, achieving a recall of 0,79 and an mAP50 of 0,85, indicating high sensitivity and precision in detection. This step is crucial for CVL diagnosis, as accurate identification of macrophages facilitates the subsequent detection of amastigotes.
Analyzing Shallow and Deep CNNs with Gold Standard and Random EEG Segment Selections for Coma Prognosis
This study focuses on deep learning models for predicting coma outcomes using electroencephalogram (EEG) data, exploring convolutional neural networks (CNNs), particularly Shallow and Deep ConvNets, based on Filter Bank Common Spatial Patterns. A dataset of 121 EEG samples (42 favorable, 79 unfavorable) was analyzed. EEG segments were selected using two strategies and frequencies. Models were trained with 10-fold cross-validation and FTSurrogate for class balance. Shallow ConvNet showed stable performance across frequencies, while Deep ConvNet excelled at 200Hz. Simple segment selection and sampling frequency methods improved CNN performance. The findings offer insights for future research and potential clinical applications.
Harnessing Self-Supervised Features for Histopathological Image Retrieval and Classification Through Efficient Fine-Tuning
This study evaluates machine learning models pre-trained using the self-supervised architectures iBOT and DINOv2 for disease/lesion classification and histopathological image retrieval. Experiments were conducted using five datasets and explored three fine-tuning strategies: Full, Low-Rank Adaptation (LoRa), and Linear Probe. The LoRa technique yielded significant effectiveness gains, improving model effectiveness by over 40% in some scenarios (LoRa vs Full). In image retrieval, the DINO-Hist model outperformed iBOT-Hist, demonstrating statistically significant superiority in MAP@10 and MAP@40. These findings underscore the adaptability of self-supervised architectures and the critical role of fine-tuning in enhancing model effectiveness.
Investigation of Deep Learning Architectures for Lesion Segmentation in Histological Images
This study evaluates the performance of convolutional neural networks (CNN)-based models and Transformers for histological image segmentation using public datasets: OCDC and GlaS. Four models were analyzed: UNet, UNet++, SharpUNet, and TransUNet, with and without data augmentation. Data augmentation proved crucial for improving model generalization, with SharpUNet and UNet++ achieving the best results in Dice coefficient and accuracy. TransUNet, despite its hybrid architecture, underperformed, possibly due to its complexity and need for large training data. The results indicate that CNN-based models, such as UNet++ and SharpUNet, are effective for histological image segmentation, especially when combined with data augmentation.
Automatic Glaucoma Detection in Retinal Images Using Convolutional Neural Networks
Glaucoma is a leading cause of blindness worldwide, affecting the optic nerve and causing progressive damage. Early detection is crucial to prevent vision loss, but manual diagnosis, based on retinal images, is subjective and prone to errors. This work proposes using deep learning with the YOLOv8n model to detect the optic disc, followed by classification with a refined ResNet-50. The method achieves 91,7% precision, 91,1% recall, 91,5% accuracy, and 82,7% kappa coefficient. We conclude that using the full retinal image outperforms using just the optic disc region, as confirmed by Grad-CAM activation analysis.
Machine Learning no Diagnóstico Parasitológico Direto de Leishmaniose Visceral: Uma Revisão
Canine Visceral Leishmaniasis is a zoonosis with a major impact on public health, whose direct parasitological diagnosis requires detailed microscopic analysis, making it an exhaustive and error-prone process. Computer Vision and Machine Learning techniques have been explored to automate this diagnosis, improving its accuracy and efficiency. This review identified that approaches based on deep neural networks and image segmentation demonstrate high potential to detect Leishmania. However, challenges such as the scarcity of public databases and the limited application of images from direct parasitological examination of bone marrow still hinder more significant advances. Implementing these technologies requires standardization and greater interpretability of the models to ensure their applicability in clinical practice.
Predicting Age and Sex from Reduced Lead Electrocardiograms using Deep Learning
Artificial intelligence is increasingly used to extract health insights from 12-lead (12L) electrocardiograms (ECG). Here, we propose a deeplearning model to predict sex and age from 12L and reduced-lead ECGs (6L–1L) and assess their impact on mortality risk. Using a ResNeXt-based model trained on the CODE15 dataset, our best models achieved an F1-score of 0.800 for sex classification (12L) and a mean absolute error of 8.961 for age estimation (4L). We found that overestimated age predictions and incorrect sex classifications were associated with higher mortality risk, whereas underestimated age predictions correlated with lower risk. These findings highlight the potential of reduced-lead ECGs for risk assessment, expanding their clinical utility.
Algorithmic Fairness in Healthcare: A Review on Bias Detection and Assessment in Machine Learning
The use of machine learning in healthcare has grown, but algorithmic biases can compromise the fairness and reliability of predictions. In this review, we analyzed 56 studies published between 2020 and 2022 to investigate how biases in predictive health models were identified and measured, as well as their potential impact. Our findings indicate that fairness and interpretability metrics have been underused, and systematic approaches to ensuring equity remained insufficient. The identified biases may disproportionately harm minority patient groups by increasing diagnostic errors, reducing treatment effectiveness, and limiting access to essential support and resources.
Investigating Epidemiological Patterns in Pediatric Oncology Through Data Integration and Visualization
This article describes a study that aimed to investigate epidemiological patterns in the DATASUS and RHC databases, based on the extraction and processing of data related to childhood cancer. From the collected, processed and integrated data, a web system was developed consisting of three dashboards, which present different types of interactive visual representations of pediatric oncology data. The system is available for public access, since the information collected from the aforementioned databases is already anonymized, and aims to assist, mainly, in strategic decision-making related to the treatment and prevention of childhood cancer.
Artificial Intelligence in Wound Characterization and Measurement: A Systematic Review
The use of artificial intelligence (AI) and software tools for wound characterization and measurement has grown significantly, offering more accurate and efficient solutions for clinical assessment. This systematic review examines studies that integrate AI and computational methodologies in wound evaluation, focusing on their effectiveness in improving diagnostic accuracy, monitoring healing progress, and optimizing treatment strategies. The findings highlight the prevalence of machine learning-based approaches, image processing techniques, and digital tools that automate and enhance traditional wound measurement methods. Moreover, the review discusses the challenges related to data standardization, model validation, and clinical adoption. The study provides a comprehensive overview of the current landscape of AI-driven wound assessment and identifies research gaps for future advancements.
A Comparative Study of Graph Neural Network Models for Drug-Target Interaction Prediction
Accurately predicting drug-target interactions (DTI) is crucial for computational drug discovery, yet there’s a research gap in evaluating existing graph neural network (GNN) models rather than developing novel architectures. This study provides a comparative analysis of three state-of-the-art GNN architectures – GraphSAGE, Graph Attention Network (GAT), and Graph Isomorphism Network (GIN) – for predicting interactions between chemical compounds and five protein targets. Using a dataset of 73,938 samples representing interactions between compounds and five protein targets derived from PubChem, we implement a robust evaluation framework with hyperparameter optimization and cross-validation. Our results show GraphSAGE achieves the highest accuracy (93%) and precision (79%), while GIN exhibits superior recall (72%). This work contributes to the field by: (1) providing a comprehensive evaluation framework for GNN models in DTI prediction; (2) offering empirical evidence of architecture-specific advantages for different application contexts; and (3) introducing a new benchmark dataset that facilitates reproducibility and further research in computational drug discovery.
Complexity-Reduced End-to-End Fetal ECG Signal Recovery and QRS Complex Detection
Non-invasive electrocardiogram (niECG) enables pregnancy monitoring by assessing the mother’s and fetus’ health through maternal abdominal signal acquisition. However, isolating fetal ECG (fECG) is challenging due to low signal-to-noise ratio and time and frequency overlap with maternal cardiac activity. Prior studies focused on fetal R-peak detection and more recent works target full fECG waveform noise-free reconstruction, but at a high computational cost, precluding real-world applications. This paper explores different ways of reducing the complexity of state-of-the-art deep learning-based method for fECG recovery and fetal QRS complex localization. Results indicate that one encoder-decoder block pair can be removed without significantly impacting the metrics. While the other complexity-reduction options yield slightly lower metrics compared to the baseline and related works, they can be fine-tuned and adapted based on the specific requirements and objectives of the application.
Comparing Video-Based Representations in Classification of General Movements of Newborns
General Movements Assessment (GMA) is a clinical method for early detection of neuromotor disorders in infants. In this study we compare video-based representations consolidated in the literature applied to classify Writhing Movements (WMs): RGB videos, optical flow, keypoints, and motion histograms. In our experiments conducted using a proprietary dataset, the RGB-based model achieved the highest individual accuracy (0.83), while the model trained using histograms obtained the best F1-Score (0.83) and efficiency. Finally, the fusion of the models via ensemble increased accuracy to 0.87. These results indicate that integrating multiple representations can enhance automated GMA analysis and contribute to the early diagnosis of neuromotor disorders.
Evaluation of the Evolution of Brazilian Municipalities' Operational Efficiency in Primary Healthcare Resource Management
The operational efficiency of Primary Health Care (PHC) units is a critical factor for optimizing resource utilization and ensuring the quality of services provided to the population. In this study, we propose an approach based on Data Envelopment Analysis to measure a dynamic efficiency index of municipalities in managing resources allocated to PHC using public data. Additionally, experiments were conducted to demonstrate how resource reallocation can impact operational efficiency, suggesting strategies for optimizing public management. Finally, we modeled the evolution of efficiency over time using Markov Chains, enabling the probabilistic analysis of future scenarios and supporting the formulation of evidence-based public policies.
Visualizing the Infection-to-Notification Dynamics of Malaria in the Brazilian Amazon
Malaria is an endemic disease in the Legal Amazon region and efforts to fight it need to consider the infection-notification dynamics of imported cases — when the notification does not occur in the same location of infection. This type of analysis can be facilitated by interactive data visualizations organized as dashboards. However, few works have proposed dashboards for analyzing Malaria data, and none of them focuses on the analysis of the infection-notification dynamics of this disease. This work presents the development and evaluation of a dashboard focused on analyzing the geolocalized infection-notification dynamics of Malaria. The dashboard was validated through use cases with real data and feedback from a domain expert. The results suggest that the tool is useful for finding patterns in Malaria data and can help authorities to better cooperate and distribute health resources between neighboring municipalities.
Dual-Scale SE UNet: A SE Block-Enhanced Convolutional Neural Network for Kidney Segmentation in CT Images
Renal cancer is one of the most common neoplasms of the genitourinary tract, making automatic segmentation an essential tool for assisting in early diagnosis. This work aims to develop and evaluate a neural network for segmenting computed tomography images and identifying kidneys, tumors and cyst. We propose replacing the standard U-Net convolutional layers with Dual-Scale SE blocks to enhance feature extraction. The proposed methodology achieved a Dice coefficient of 0.93 and 0.86, respectively.
Evaluating Deep Neural Skin Cancer Classifiers With Multiple Images Inputs
Skin cancer represents one-third of all globally diagnosed cancers. Despite its generally low mortality rate, late diagnosis remains a significant cause of complications. To mitigate such risks, Computer-Aided Diagnosis (CAD) systems have been developed to provide more accessible and timely diagnostic methods. While the CAD field has demonstrated considerable promise, most existing systems rely on a single image of the lesion, and the impact of using multiple images has not been extensively studied. This work aims to investigate how incorporating multiple images affects the efficiency and accuracy of CAD systems. Specifically, we evaluate the performance of three different deep learning models integrated into a stacking-like strategy that processes multiple image inputs. Notably, we achieved a 6% increase in balanced accuracy, without adding significant training or testing burdens to the existing models.
Slice Selection Guided by Grad-CAM in CT Scans for Brain Hemorrhage Detection
Intracranial hemorrhages are severe and potentially fatal conditions that require rapid and accurate diagnosis to reduce neurological sequelae and mortality. Computed tomography (CT) is widely used for this detection, but manual evaluation is time-consuming and subject to interobserver variability. Three-dimensional convolutional neural networks (CNN3D) have been applied to assist in volumetric medical image analysis. However, their use is hindered by high computational cost and redundant information in full CT volumes. This study proposes Gradient-weighted Class Activation Mapping (Grad-CAM) 2D Guided Selection (SGG-2D), a hybrid approach that combines adaptive and non-adaptive techniques for efficient slice selection. The method employs a 2D classification model with Grad-CAM to identify the most informative slices, complemented by a structured sampling strategy that ensures a balanced distribution of selected slices across the tomographic volume. The approach was compared against six existing methods, achieving the highest accuracy (75.67%) and AUC-ROC (82.72%). The results demonstrate that optimized slice selection not only reduces computational complexity but also enhances the quality of data input for CNN3Ds, leading to improved accuracy in anomaly classification in volumetric exams.
Towards a Personalized mHealth Model Using Intelligent Conversational Agents
Mobile health (mHealth) applications are increasingly used for health monitoring but often struggle to engage users effectively. This study proposes a computational model to personalize mHealth experiences using intelligent conversational agents that combine Large Language Models (LLMs), vector-based knowledge retrieval, and sentiment analysis. An experiment was conducted to evaluate the model’s application in assisting patients with urinary incontinence after radical prostatectomy. The results demonstrated that incorporating a specialized knowledge base significantly improves the personalization of clinical support. The study underscores the potential of LLMs to improve user experience in healthcare applications and highlights the need for further refinements in adapting interaction strategies to better meet patient needs.
Physio Games – A Serious Game Environment for Physical Rehabilitation of Amputees
This article proposes a computational solution based on a serious game, incorporating both hardware and software, to assist physiotherapists in rehabilitation sessions for patients with amputations. The approach employs a sensor node to capture patient movements, process the data, and transmit it via Bluetooth to an integrated environment developed using the Godot game engine, chosen for its capability to operate across multiple platforms. Additionally, the solution includes a WebApp designed for patient data visualization and physiotherapy session tracking. Beyond supporting physiotherapists, the game aims to enhance patient engagement. Initial evaluations conducted with physiotherapists yielded promising results regarding the solution’s applicability.
Instance Segmentation in Medical Imaging: A Comparative Study of CNN and Transformer-Based Models in a Teledermatology Study-Case
The rapid evolution of instance segmentation models necessitates empirical comparisons to guide their adoption in critical domains like medical imaging. This study evaluates four state-of-the-art architectures—Mask R-CNN, Mask2Former, YOLOv11, and YOLOv12 on a teledermatological dataset annotated for compliance-driven segmentation of rulers and patient information tags. Results demonstrate that transformer-based and hybrid models (Mask2Former, YOLOv11) significantly outperform traditional CNNs in precision-driven metrics (AP75), highlighting their suitability for medical applications. This work provides actionable insights for model selection in healthcare, emphasizing the balance between accuracy and computational efficiency.
A Visual Perception-Based Approach to Support Cervical Cytology Learning
The emergence of new hardware and software has allowed the improvement of activities in different contexts by including technological resources to support the execution of these activities. One context that benefits from this improvement is education since these resources allow exploring various strategies to enhance the teaching-learning process. This incorporation of technology into regular activities also aligns with the expectactions of users, who expects technological resources to be integrated into their daily lives. This work presents a computational approach to support the teaching-learning process of content based on visual perception in cytology. To achieve this goal, we designed a framework. We implemented a proof of concept to help evaluate the model and build the database necessary for activities in the context of cytology. Users with knowledge of the area evaluated the framework. The resultsdemonstrate that the interaction model developed could represent real-world activity and expose students to specific unusual situations. The evaluation collected information and indications on how the tool can improve the teaching-learning process.
Estimating Respiratory Rate Using Wi-Fi CSI Data Compared to Commercial Device Measurements
Technological advancements have driven the development of various devices capable of estimating vital signs, such as respiratory rate. Despite the availability of these devices, they still present disadvantages, such as the discomfort generated during respiratory rate estimation due to the need for physical contact with the patient. In this context, Channel State Information (CSI) technology has emerged as a promising alternative. It allows the monitoring of Wi-Fi signals, which contain information about the transmission state, to estimate the breathing rate in home and hospital environments. In this work, we present the application of CSI, along with signal processing techniques, for estimating the respiratory rate in individuals. To validate our approach, we compared the respiratory rate estimates obtained with CSI to those of commercially validated devices, such as Polar H10 and Biopac. Our database consists of approximately 100 volunteers. The obtained results demonstrate a high similarity between the CSI estimates and those of the validated commercial devices.
Retinography Report Generation Using Contrastive Captioner
Automatic generation of fundus reports acts as medical support, allowing the diagnosis of eye diseases more quickly when compared to traditional methods, reducing the waiting time of patients with eye diseases and contributing to the reduction of cases of visual impairment. Recent models of report generation propose new methods for integrating visual and textual information, presenting dependence on keywords for the generation of clinical descriptions. In this work, we explore the pre-trained Contrastive Captioner (CoCa), aiming to correlate image and text by combining the two loss functions present in the model, with the objective of generating fundus reports without depending on keywords. In the experiments performed on the DeepEyeNet dataset, the method achieved a BLEU-4 of 0.230, CIDEr of 0.517, and METEOR of 0.423.
Pampacare Ae Ae: An App for Dengue Management and Tracking
Dengue is a disease transmitted by the bite of the Aedes aegypti mosquito, which, in some cases, can lead to human fatalities. This work presents the proposal and development of a mobile application aimed at digitizing the process of reporting dengue cases and mosquito breeding sites, carried out by endemic disease control agents, with a focus on usability and practicality. Some proposed features include: digitization of dengue case and mosquito breeding site reports, offline functionality, automatic address filling based on location, and reports generated from the entered data. With the completed application, it is believed that it can make processes more efficient and assist public authorities in decision-making for disease control.
Deep Endometriosis Segmentation in MRI Using CNN Encoders in a U-Net Architecture
Deep endometriosis is a globally prevalent pathology that affects around 20% of women of reproductive age and afflicts the patient with symptoms ranging from from abdominal pain to bleeding and infertility. Its diagnosis can be made by imaging tests, which, combined with machine learning models, represent a significant improvement in this process. This paper brings an automatic approach to endometriosis segmentation in magnetic resonance images, obtaining a Dice of 51% and a Recall of 66,58%.
A Transformer-Based Methodology for Person-Independent Sweeping Activity Recognition Using Wi-Fi CSI Data
The use of channel state information (CSI) for human activity recognition holds promise in healthcare, especially for remote patient monitoring. By capturing and interpreting Wi-Fi signals in indoor environments, CSI can be used to detect physical activity, falls, or daily movements of a patient, allowing caregivers and healthcare professionals to monitor patients without the need for wearable sensors or invasive cameras. CSI also has great potential in elderly care. Therefore, this paper proposes a methodology called DVC-CSI to identify the floor sweeping activity of a person in a room through the analysis of CSI data and a dataset used for its evaluation. DVC-CSI uses Transformer models developed to process time series data featuring a structure that allows capturing temporal dependencies. DVC-CSI is capable of identifying activities of people who did not participate in the training phase. The accuracy of floor sweeping activity identification is 88.89% using a CSI dataset of 86 volunteers (60 participants for training, 17 participants for validation, and 9 participants for testing).
A Tool for Heuristic Usability Evaluation in Mobile Applications from a Digital Health Literacy Perspective
Heuristic evaluation based on inspection is a widely used method by experts to test the usability of interfaces. Despite its effectiveness, heuristic evaluation can be a time-consuming process, especially in systems with a large number of screens or detailed functionalities. To optimize this process, this research proposes the development and use of FAU-MH (mHealth application usability evaluation tool), designed to support experts in evaluating the usability of mobile health applications. To validate the effectiveness of FAU-MH, an experimental study was conducted with computing specialists, divided into two groups. The findings indicate that FAU-MH has an intuitive interface and significantly contributes to accelerating the heuristic evaluation process.
Implementation of a Mathematical Model for Synthetic ECG Signal Generation Using Low-Cost Hardware
The generation of synthetic electrocardiographic signals is a promising approach to overcoming challenges in acquiring real signals, such as environmental interference, the scarcity of rare disease records, and ethical restrictions on clinical data usage. This work implements a mathematical model combined with a low-cost digital-to-analog converter, utilizing Pulse Width Modulation (PWM) and a low-pass filter. The model simulates different cardiac conditions, such as atrial fibrillation. The results confirm the fidelity of the synthetic signal, highlighting its applicability in equipment calibration, education, and the enhancement of artificial intelligence algorithms for automated diagnosis.
Fetal Hypoxia Detection Exploring Time Series Representations with Convolutional Neural Networks
This study explored the use of Recurrence Plot (RP) and Poincaré Plot (PC) as inputs for Convolutional Neural Networks (CNNs) in detecting fetal hypoxia from cardiotocography data. The experiments showed that RP achieved better overall performance (Sensitivity (Se) = 61.98% ± 10.9; Specificity (Sp) = 63.58% ± 11.2), being more effective in detecting critical patterns, especially in 15-minute segments. PC demonstrated greater stability in longer segments (Se = 65.62% ± 6.6; Sp = 61.17% ± 12.3), however was generally less effective in identifying hypoxia. The results suggest that RP is more suitable for capturing the nonlinear dynamics of fetal heart rate in automated monitoring systems.
IaraMed: A Women’s Healthcare Chatbot for Portuguese Speakers
The advent of large language models has significantly advanced natural language processing, revolutionizing numerous applications within the healthcare domain. Despite these advances, most existing research remains predominantly centered around English, resulting in notable disparities in medical AI accessibility for non-English speaking communities. To bridge this gap, we introduce a specialized Portuguese-language chatbot tailored explicitly to women’s healthcare needs, addressing critical shortages in linguistic resources and culturally relevant data. Leveraging retrieval-augmented generation, our chatbot integrates accurate, evidence-based information directly into generated responses. Evaluations demonstrate that our approach markedly enhances reliability, highlighting the potential for tailored AI applications to significantly improve healthcare accessibility and outcomes for Portuguese-speaking women.
Generalization of Cardiomyopathy Classification Models Based on Feature Descriptors from Magnetic Resonance Imaging
Supervised Machine Learning (SML) models can help physicians to compose more accurate diagnoses. Due to the diversity of machines, systems, and protocols, exams conducted at different centers may have high variability, decreasing the generalization ability of these models. This paper aims to evaluate generalization ability of SML models for cardiomyopathy classification in Cardiac Magnetic Resonance Imaging (CMRI), using a set of left ventricle morphological and motion features. We performed cross-validation tests on two public CMRI databases, comparing intraand inter-dataset performances. The results are promising and demonstrate that the implemented features can contribute to building generalizable classification models.
A Web System for Automated Image Diagnosis Supporting Different Microscopic Classification Models
Neglected diseases, such as Leishmaniasis, primarily affect tropical regions and can have a severe impact on public health, especially in developing countries. Based on this premise, this study proposes a low-cost system for the automated capture and classification of images from microscopy examinations, capable of supporting multiple models for detecting different diseases, including Human Visceral Leishmaniasis (HVL) and Canine Visceral Leishmaniasis (CVL). The deep learning models integrated into the system — InceptionV3 for HVL and YOLOv8 for CVL — achieved accuracies of 98,7% and 82,2%, respectively. Additionally, an automated image capture platform was developed, enabling standardized and cost-effective acquisition of microscopic images. Usability tests demonstrated high user acceptance of the system, highlighting its intuitiveness and efficiency in slide analysis.
Texture classification in medical images through generative models and self-supervised learning
This work proposes a regularized Variational Autoencoder for texture analysis, composed of an encoder, a decoder and a predictor module, with a triple loss function that simultaneously regularizes the image encoding, its reconstruction, and classification. The method presents state-of-the-art classification accuracy for mammographies and generates more separable latent spaces that may contribute to texture analysis.
Knee osteoarthritis classification in X-Ray images using Ensemble Learning
Knee osteoarthritis (OA) is an inflammatory disease that can cause deformity of the articular cartilage, among other symptoms, and whose accurate diagnosis is essential to predict its progression. This work proposes an ensemble of deep neural networks such as ResNet50, Xception, Inception ResNetV2 and EfficientNet to classify OA from X-ray images, according to the levels established by the Kellgren Lawrence scale. A preliminary analysis indicates that the ensemble model surpasses results obtained by individual networks, providing a more assertive classification of the different stages of the disease.
Gender Disparity Mitigation in Multitask Neural Network Regression Models Applied to Parkinson’s Disease
Fairness in predictive models is crucial, especially in sensitive contexts such as healthcare, where disparities can result in severe consequences. This work proposes a solution to mitigate such disparities in regression problems. The approach employs a modified multitask neural network, in which modifications are made to the loss function to reduce the disparity in errors between genders. The model was applied to a dataset of patients with Parkinson’s disease to predict a score on disease progression. Preliminary results show the ability to predict gender and satisfactory performance in the regression task.
Innovation and Transformation in the Hearing Health Ecosystem: Artificial Intelligence and Interoperability
With the advancement of artificial intelligence and the increasing digitalization of health systems, interoperability emerges as a critical factor for integrating different sources of information and improving the effectiveness of audiological services. However, challenges such as data fragmentation, technological barriers, and system heterogeneity still limit the adoption of solutions using AI and standardized databases in the sector. This study presents gaps in the area and proposes the development of a component integrated into a digital hearing health ecosystem, with the aim of automating processes, enabling the interoperability of audiological data, and improving data-based decision-making.
Modeling Biomedical Time Series: A Comparative Study Between PSTA-TCN and Vanilla TCN for Glucose Data Forecasting in Diabetic Patients
This study compares the performance of one-dimensional convolutional layer-based models, PSTA-TCN and Vanilla TCN (keras-tcn) in one-step-ahead prediction of glucose levels in diabetic participants. Continuous glucose and heart rate measurements were used as predictor variables, and the models were evaluated using metrics such as MAE, RMSE, and training time. Continuous heart rate and glucose data were collected from 20 diabetic volunteers over a period of 3 months. The results show that Vanilla TCN achieves lower predictive error, while PSTA-TCN presents higher computational efficiency.
SPIRA-BM: Biomarkers for Respiratory Conditions by Audio Analysis via Artificial Intelligence
This paper presents an ongoing project that aims to investigate audio biomarkers for respiratory conditions such as Respiratory Insufficiency, severe asthma, and smoking. The SPIRA-BM project is currently developing inexpensive detectors for these biomarkers, using audio collection and processing on mobile computing devices, and using machine learning, artificial intelligence, and signal analysis techniques.
Low-Cost Assistive Videolaryngoscope for Teaching and Training Intubation Procedures
This paper introduces an innovative assistive video laryngoscopy system to enhance medical training and procedural visualization. It was built under the IoT paradigm by integrating hardware, software, sensors, and a camera within a traditional laryngoscope via a wireless network. It offers real-time data and enhanced visuals. Preliminary evaluations, informed by interviews with nurses, indicate that the system adheres to functional and performance requirements, demonstrating its potential to improve healthcare professionals' instrument handling skills.
Can Data Complexity Measures Detect Pre-Training Bias in Machine Learning? A Case-Study with Health Data
Bias in healthcare data can negatively impact vulnerable populations and reduce the reliability of predictive models. This work investigates the use of data complexity measures to identify features that may introduce bias during model training with machine learning (ML) algorithms. We analyze a synthetic dataset on schizophrenia and depression and a real dataset on liver disease, evaluating complexity across subgroups defined by protected attributes such as sex and race. The approach is validated against traditional pre-training bias metrics. Preliminary results suggest that data complexity measures can serve as an early indicator of bias, supporting the development of fairer and more transparent predictive models. This framework could inform bias mitigation strategies to improve model fairness in health-related ML applications.
Binary Classification of MRI Images of Osteoarthritis with a Modified R3D_18 Model
This work proposes a 3D convolutional neural network model derived from the R3D_18 architecture to classify magnetic resonance images (MRI) of the knee, differentiating normal and abnormal images associated with osteoarthritis. Using the OAI-MRI-3DDESS dataset, the model was trained with cross-validation and oversampling to compensate for class imbalance. The approach aims to improve diagnostic accuracy and reduce professionals’ workload by exploring the volumetric information of the images. The experiments indicated robust performance, with accuracy higher than 86% and AUC close to 0.92, showing the method’s potential for clinical applications.
Digital solutions for health services management in brazilian communities: initial results of a scoping review in SBCAS publications
Health services and systems face increasing pressure to improve management efficiency and quality, especially amid economic constraints and rising demand. This study investigates digital solutions for health services management presented at SBCAS. We conducted a systematic search and selection in SBC-OpenLib using keywords related to health management and administration, resulting in a set of 26 articles. Preliminary results indicate that the solutions focus on data analysis, resource allocation, predictive modeling, and hospital administration. This ongoing study suggests that these technologies provide strategic support for managers, highlighting the need for greater integration between technological innovation and management practices.
iTB: A Technological Solution for Tuberculosis Management and Monitoring in Primary Care
Tuberculosis (TB) is a public health challenge requiring innovative solutions to optimize patient management and monitoring. The iTB system, developed by the Municipal Health Department of Manaus using the Kanban agile methodology and validated in a pilot project across three Family Health Units, integrates data from the PEC e-SUS and SINAN systems. This tool enhances data quality, enables real-time medication dispensing, and facilitates clinical decision-making, being implemented in 216 health units. Despite connectivity challenges in remote areas, iTB demonstrates potential to optimize TB management in Primary Health Care (PHC).
Bayesian optimization of a laser-plasma accelerator aiming the production of high-energy electron beams for VHEE radiotherapy
Radiation therapy aims to maximize tumor dose while minimizing exposure to healthy tissues. Despite advances, normal tissue toxicity remains a significant obstacle. While proton therapy shows promise, its high cost limits widespread adoption. Very high-energy electrons (VHEE, 50-250 MeV) represent an alternative with deep tissue penetration suitable for conventional and FLASH radiotherapy, which enables higher curative doses while reducing normal tissue toxicity. Laser-plasma accelerators (LPAs) offer a compact and costeffective approach to generating electron beams for VHEE radiotherapy, a modality with dosimetric advantages comparable to proton therapy but without requiring the large cyclotrons typically used for ion acceleration. This work implements Bayesian optimization to systematically tune particle-in-cell (PIC) simulations modeling an LPA, aiming to produce optimal electron beams for VHEE radiotherapy. Key findings include achieving a spectrum with two charge peaks at different energies and total integrated charge of 1.9 nC. Additionally, the Institute of Energy and Nuclear Research (IPEN) has recently received approval to acquire a 15 TW laser, making it the most powerful in the Southern Hemisphere. The framework developed in this work can be scaled to IPEN’s laser system parameters to guide future VHEE radiotherapy experiments.
Proposal of a System for Plantar Pressure Analysis Using Pressure-Sensitive Insoles
This paper presents the evolution of the Foot Inspect system, a computational tool for analyzing plantar pressure and creating customized insoles. The initial proposal used an Arduino microcontroller and pressure sensors, while the new version uses a NodeMCU ESP32 with Bluetooth communication, Force Sensitive Resistors (FSR) sensors, which are capable of measuring forces ranging from 0 N to 445 N, and a desktop application for data processing. The system generates heat maps of plantar pressure using geostatistical techniques, such as Kriging, and integrates with a database and a web interface to facilitate the diagnosis and treatment of foot deformities.
Exploring the Use of Vision Transformer in the Classification of Anal and Cervical Lesions: A Systematic Review
Although anal canal cancer (ACC) has a low incidence among gastrointestinal tract tumors, a significant increase in the number of cases has been observed, making it an emerging public health concern. This paper presents a systematic review of the use of the Vision Transformer architecture in classifying cervical and anal cells, evaluating its performance compared with other machine learning architectures. Cervical cells were included in the study due to their histological similarity to anais cells and the widespread use of the Pap test. The objective is to identify the state of the art in the diagnosis of cervical cancer through conventional cytology and to explore the feasibility of using transfer learning for application in anal cancer.