Companion Proceedings of the Brazilian Symposium on Computing Applied to Healthcare (SBCAS)
Sociedade Brasileira de Computação (SBC)
Exploring Biases in Machine Learning Models for Neurodegenerative Diseases Diagnosis Through Gait and Voice Analysis
This work examines potential biases in machine learning models for diagnosing neurodegenerative diseases (NDDs) through gait and voice analysis. It investigates how common techniques, such as gait signal windowing and using multiple voice samples per individual indiscriminately, can lead to inflated performance estimates when samples from the same individual are treated independently. Using two public databases, it compares scenarios where augmented samples are treated independently versus grouped by individual. Results show that independent treatment leads to artificially higher performance metrics. The findings highlight the need for proper intra-individual variability handling to ensure reliable clinical decision support for NDD diagnosis.
An algorithm for deduplication and insurance of quality in a database for ASD diagnosis: proposal and qualitative evaluation
Autism Spectrum Disorder (ASD) diagnosis requires the action of well-trained health professionals, which limits access to diagnosis. Computer-aided diagnosis using biomarkers can be an alternative to make diagnosis more accessible. However, public databases to support the development of CAD systems are still a challenge. This study presents an algorithm to identify flaws in data quality in a database for ASD diagnosis, such as duplicate records and missing data, in the Research Electronic Data Capture platform. The tool automates error detection and generates structured reports to assist health professionals in correcting data. A qualitative evaluation confirmed its usefulness and indicates that the time to identify errors can decrease approximately 15 times, contributing to minimizing the effort necessary to maintain a consistent database.
Impact of balancing and regularization on semantic segmentation of histopathological images
This study investigates the impact of class balancing and regularization on improving diagnostic agreement in histological images. For example, U-Net models applied to the Prostate Cancer Grade Assessment dataset show that class balancing, combined with traditional loss functions, increases image agreement by up to 6 percentage points. Combining balancing with Focal Loss boosts classification agreement by an average of 13 percentage points compared to using imbalanced datasets with traditional loss functions. A case study on the analysis of prostate Gleason patterns 3 and 4 illustrates the importance of this discussion to clinical decisions and the prognosis of prostate cancer patients.
Heart failure classification in echocardiograms using Deep Neural Networks
This article presents a deep learning model based on MobileNetv4 for detecting heart failure in echocardiograms based on left ventricle ejection fraction (LVEF) estimation. The pipeline consists of two stages: a regression model predicting ventricular volume coordinates, and a classification model that computes LVEF from the predicted volumes. A 5-fold cross-validation was performed, yielding a coefficient of determination R² of 0.83 ± 0.01 (95% CI: [0.83, 0.84]) and an accuracy of 0.74 ± 0.01 (95% CI: [0.74, 0.75]).
“Error factory”: a framework for simulating errors in left ventricle segmentation in cardiac images
Left ventricle segmentation in Cardiac Magnetic Resonance exams is important for medical diagnosis. Deep learning methods have excelled in obtaining segmentations similar to those of experts. However, one of the current limitations is the arbitrary production of anatomical errors that can compromise the diagnosis. Given this problem, this work proposes a framework for categorization and controlled automatic simulation of different anatomical errors. The framework favors the development of methods aimed at detecting and correcting these errors. Results indicate that the proposed framework is capable of generating errors of several categories and replicating the same errors produced by networks (Dice > 0.8).
Dynamic Analysis of Hospital Data: An Interactive Approach to Health Management
The growing volume of data from DATASUS, including hospital admission records, presents challenges such as a lack of standardization and incompleteness. Visual exploratory analysis emerges as an effective tool to facilitate its interpretation and assist in decision-making. This work aims to apply interactive visualization and data exploration techniques to analyze hospital occupancy in Rio Grande do Sul, providing an online dashboard that allows queries about hospitalizations in the region. The tool enables the analysis of, for instance, the most common procedures and the distribution of hospitalizations over a year, providing information for a more efficient allocation of hospital resources.
Generating Synthetic Magnetic Resonance Images for Deep Learning Applications with Limited Labeled Data
This study addresses medical imaging data scarcity in AI development by using Progressive Growing of GANs (PGGAN) and transfer learning (TL) to generate synthetic MRIs of frontotemporal dementia variants. Our pre-trained foundation model achieved high anatomical fidelity (FID: 21.6008), while TL significantly improved image quality compared to models trained from scratch, reducing FID scores from ~115 to ~40. Although clinical validation is still needed, this framework offers a promising approach to expand limited medical datasets while maintaining anatomical accuracy for rare disease analysis.
Machine Unlearning Analysis in Medical Image Classification Models
Machine unlearning aims to remove private or sensitive data from a pre-trained model while preserving the model’s robustness. Despite recent advances, this technique has not been explored in medical image classification. This work evaluates the SalUn unlearning model by conducting experiments on the PathMNIST, OrganAMNIST, and BloodMNIST datasets. We also analyze the impact of data augmentation on the quality of unlearning. Results show that SalUn achieves performance close to full retraining, indicating an efficient solution for use in medical applications.
Application of Deep Learning Techniques for Diabetic Retinopathy Classification
Diabetic retinopathy (DR) is a degenerative condition of the retina caused by diabetes. Early diagnosis is crucial to prevent disease progression and preserve vision. However, traditional diagnostic methods require a thorough analysis of retinal images by experts, making the process time-consuming and subjective. This study proposes a deep learning model for the automated classification of DR, based on the analysis of different convolutional neural networks and image processing techniques. The results indicate that the combination of preprocessing using CLAHE and the ResNet50 with the ADAMW optimizer achieved the best performance, with an accuracy of 0.83, ROC-AUC of 0.87, and a Kappa coefficient of 0.85.
An explainable model for cardiac arrhythmia classification using LSTM networks
Arritmias cardíacas são anomalias nos impulsos elétricos do coração que resultam em um ritmo alterado, podendo indicar doenças cardíacas potencialmente fatais. No Brasil, essas doenças são a principal causa de morte e, globalmente, representam 16% dos óbitos. O diagnóstico precoce é essencial para aumentar as chances de tratamento e reduzir a mortalidade. O principal exame utilizado é o eletrocardiograma (ECG), um método de baixo custo e rápida obtenção de resultados. No entanto, a identificação de arritmias enfrenta desafios, como a necessidade de análise criteriosa por profissionais especializados, possíveis divergências nas interpretações, interferências nos sinais do ECG e a baixa sensibilidade na detecção de algumas doenças. Para superar essas limitações, sistemas automatizados baseados em aprendizado profundo demonstram um enorme potencial, proporcionando uma análise mais precisa dos sinais cardíacos e aumentando a assertividade do diagnóstico. Dentre as abordagens mais promissoras, redes neurais profundas recorrentes (RNNs) têm demonstrado um excelente desempenho na análise de séries temporais de ECG. Este trabalho obteve 92% de acurácia no conjunto teste classificando 5 classes. Aplicamos o framework SHAP para validar as respostas da rede, fazendo com que a explicabilidade aprimorasse a confiabilidade na identificação e na classificação de arritmias cardíacas. Os pontos destacados pelo modelo como importantes para o diagnóstico da rede foram coerentes com a análise feita pelo especialista da área.
Reinhard it: Normalization and Classification on HER2 images
HER2-positive breast cancer is one of the most aggressive subtypes and among the most frequently diagnosed. It results from the overexpression of the HER2 protein, which is assessed using the ImmunoHistoChemistry (IHC) score. However, this evaluation is often performed manually, creating opportunities for automation. In this context, this study investigates the impact of the Reinhard technique compared to mean and standard deviation normalization methods to quantify protein levels. The results demonstrate the potential of the proposed approach, achieving an F1-score of 0.89, precision of 0.89, and recall of 0.90, whereas the mean and standard deviation normalization method obtained 0.85 for precision and 0.86 for both F1-score and recall.
Investigating Cardiac Magnetic Resonance Imaging Feature Descriptors for Generalization of Cardiomyopathy Classification Models
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 model generalization ability. 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.
PaNasc: A computational solution for data extraction and analysis from SINASC
The availability of data on live births is essential for monitoring public health and making data-driven decisions. However, the tools provided by DATASUS, such as Tabwin and Tabnet, have limitations and are difficult to access for users without experience in database systems and management. To address this issue, PaNasc was developed.PaNasc is a project that facilitates access to and analysis of SINASC data by creating a process written in the R programming language that performs ETL on these data and provides an intelligence dashboard with fast and interactive analyses of these indicators.
A Prototype for Interactive Information Visualization as Support for Computer-Aided Diagnosis
Computer-Aided Diagnosis (CAD) can offer a second opinion to help physicians compose a more precise diagnosis, and interactive Information Visualization (IV) tools can complement CAD systems by providing a more intuitive way to interpret data from patients and exams. In this study, we developed an interactive IV prototype to improve comprehension of a flexible database for a CAD system in the cardiomyopathies context. This prototype is integrated with a web application to retrieve Cardiac Magnetic Resonance exams, and a qualitative evaluation conducted with health professionals showed that IV provided valuable data analysis capability. The results have shown that the integration of advanced visualization tools in our developed prototype can enhance clinical decision-making, improving the effectiveness of CAD systems.
Detection of Micronuclei in Lymphocytes: A New Dataset and Case Study Using YOLOv11
The detection of micronuclei (MN) in lymphocytes is essential in biomedicine for assessing genetic damage and chromosomal instability, playing a crucial role in toxicological studies and cancer diagnosis. However, manual identification is time-consuming and prone to errors. In this work, we propose an automated approach using the YOLOv11 neural network for detecting micronuclei in binucleated cells. To achieve this, we built an image dataset collected from the Regional Center for Nuclear Sciences of the Northeast (CRCN), comprising 889 images annotated with the locations of binucleated cells and micronuclei. The results of the detection analysis show that the model achieved a precision of 90.8% and a recall of 92.8%, demonstrating reliability for clinical applications. Additionally, the developed dataset contributes to future research in the field, providing a standardized benchmark for evaluating computer vision models applied to cytogenetics. The dataset is available at https://doi.org/10.5281/zenodo.14947933.
Using Recurrent Neural Networks to Predict Ischemic Heart Diseases from Time Series
The present investigation aims to evaluate the efficiency of using recurrent neural networks to analyze a time series of deaths due to ischemic heart disease in the city of São Luís, Maranhão. It employs two models, LSTM, BiLSTM, and GRU, seeking to identify the combination of parameters that yields the best results. These RNNs are compared with the traditional ARIMA model in an experiment using k-fold. Results indicate that LSTM achieves an RMSE of 0.70, the BiLSTM of 0.45, and the GRU of 0.46, while ARIMA reaches a higher RMSE of 7.5.
Detection of Chromosomes in Metaphases: A New Dataset and Case Study Using YOLOv11
Despite advances in automatic chromosome detection, most existing studies use private datasets, which limits direct performance comparison between different methods. To address this gap, this work develops a new dataset composed of 519 metaphase cell images with annotated chromosome locations, using samples collected at the Regional Center of Nuclear Sciences of the Northeast (CRCN-NE). Additionally, three variants of YOLOv11 (YOLOv11n, YOLOv11s, and YOLOv11m) are evaluated using Mean Average Precision (mAP), Precision, and Recall metrics. The results show that YOLOv11m, with 1024×1024 pixel images, achieved the best performance, reaching mAP@50 of 99.24% and mAP@50-95 of 74.86%. These findings highlight the potential of YOLOv11 in automating chromosome detection, providing greater accuracy and efficiency in cytogenetic analysis. The dataset is available at https://doi.org/10.5281/zenodo.15101359.
3D Reconstruction from Thermographic Images: Evaluation of 3D Models and Breast Symmetry
This research performs digital breast supervision in 3D from 2D thermographic images and develops an objective methodology to evaluate these reconstructions, based on 24 measurements consider lengths, areas and volumes. The results indicated differences of up to 7.5% in relation to thermal images and 6% in comparison with scans. In addition, a classification of breast symmetry is proposed using 12 indexes, with an accuracy rate of 60%. The method demonstrates potential to expand the way thermal exams are viewed, in addition to contributing to the classification of breast symmetry.
Comparing Classical Ordinary Differential Equation and Neural Network Models for Reduced-Order Single-Cell Electrophysiology
Electrophysiology modeling is key for non-invasive diagnostics and understanding heart and brain function. Traditional models use ODEs, from detailed ion channel dynamics to reduced-order phenomenological models. We compare a fast reduced-order model with data-driven and physics-informed neural networks as efficient alternatives to numerical solutions. Using the FitzHugh-Nagumo model, we trained networks with numerical data and model physics, employing architecture optimization, adaptive point density, and time-domain splitting. Inference via TensorRT achieved up to 1.8× speedup over optimized CUDA solvers with minimal accuracy loss. These results showcase neural networks as viable emulators when complexity is controlled.
Deep learning applied to non-invasive ECG pregnancy monitoring: fetal ECG recovery and QRS complex detection
Non-invasive electrocardiography (niECG) is an option to mitigate the limitations of the current pregnancy monitoring methods. Still, one of the main challenges in its use is the extraction of the fetal signal (fECG) from the measured abdominal signal (aECG) due to the low signal-to-noise ratio and the overlap of the maternal and fetal QRS complexes in time and frequency domains. We present two encoder-decoder deep learning models that use regions of interest (RoI) to perform simultaneous fetal QRS complex (fQRS) detection and fECG recovery. We show that such a RoI-based end-to-end approach leads to robust state-of-the-art results in inter-subject and cross-dataset evaluations.
Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph
Accurate disease prediction is critical in healthcare. This study introduces Graph Neural Networks (GNNs) and Graph Transformer (GT) models to predict heart failure (HF) incidence at the next hospital visit using a patient similarity graph. Using MIMIC-III Electronic Health Records (EHR) data, we constructed a patient similarity graph via K-Nearest Neighbors (KNN) with embeddings from diagnoses, procedures, and medications. We implemented GraphSAGE, GAT, and GT to predict HF, evaluating performance with F1, AUROC, and AUPRC metrics against baseline models. A three-axed interpretability analysis explored model decision-making. GT achieved the highest performance (F1: 0.5361, AUROC: 0.7925, AUPRC: 0.5168). While Random Forest (RF) showed a similar AUPRC, GT provided superior interpretability by leveraging patient relational information. Analyzing attention weights, graph structure, and clinical features offered deeper insights into classification decisions.
Development of an Evaluation Method for Mobile Health Apps – The QRAMHe Method
With the growing supply of health applications, it is necessary to guarantee reliable information that encourages adherence to treatment recommended by health professionals and minimizes the risks associated with inaccurate or inadequate information. However, the absence of structured methods to evaluate these applications makes their selection difficult. To fill this gap, a method was developed based on scientific literature, the needs of users and professionals, best practices and scientific evidence. The approach combines systematic literature review and experimental development, resulting in a questionnaire validated by experts with Fleiss' Kappa analysis confirming its satisfactory reproducibility.
An Artificial Intelligence-Based Method for Estimating Weight and Height from Multi-View Clinical Images
This study proposes a method based on deep learning and computer vision to automate height and weight measurement, reducing human-related errors in clinical assessments. Inaccurate measurements can lead to dosage and diagnostic errors, affecting the patient’s health. A total of 19,172 clinical images from multiple views (posterior, anterior, and lateral) of 4,793 individual evaluations were used. The DeepLabV3 model extracted silhouettes, which were then used to train the WideResNet and Scale Equivariant WideResNet architectures. The approach combines multi-view silhouettes, specialized architectures for scale invariance, and regularizers that consider the relationship between weight and height. The method achieved a Mean Absolute Error of 3.7 kg and 3.6 cm for weight and height, respectively, based on 3,834 images or 958 evaluations in the test data. The best results were obtained where the Mean Squared Error (MSE) was the lowest. The results demonstrate the effectiveness of this approach in obtaining precise biometric measurements, highlighting its potential to enhance clinical accuracy and support patient health monitoring.
Detecting Signs of Depression in Portuguese Texts Considering Brazil’s Cultural and Regional Aspects Using LLMs
This study investigates the identification of depression traits in messages from the social network Reddit, focusing on young people and adolescents. To this end, two lexicons were compared: one with terms in Portuguese without cultural expressions and another culturally enriched lexicon, constructed from Brazilian song lyrics. Using the GPT 3.4 and Flash 1.5 models, we analyzed messages labeled as depressive and non-depressive, classified by a mental health specialist. The results show that the cultural adaptation of the lexicon allowed a more sensitive and accurate analysis, suggesting the importance of regional lexicons to improve the detection of signs of depression in specific cultural contexts.
B-Track: A model for assisting in non-communicable diseases through human behavior analysis
Chronic diseases are among 7 out of the 10 leading causes of death worldwide. The main chronic diseases are heart disease, cancer, chronic respiratory diseases, and diabetes. Heart disease alone causes 9 million deaths a year. Lifestyle changes can prevent many chronic diseases’ deaths and their risk factors. In addition, machine learning and wearable devices have been used for behavior analysis. Therefore, this research proposes B-Track, a computational model for assistance in chronic disease care through analyzing behaviors that attenuate or worsen the risk factors associated with chronic diseases, working with user behavior profiles and recommendations for healthier behaviors. Besides, an ontology was created to be used as a knowledge model for the B-Track model to track behaviors associated with risk factors for chronic diseases. The B-Track collects data from different data sources for current and future human behavior analysis through the usage of data fusion and machine learning models. These data comprise the patients’ context histories, which include sensor data and data from self-management surveys. Based on the ontology inferences, the B-Track acts in a personalized manner, sending recommendations for habit changes to patients and allowing user accompaniment by health professionals. The scientific contribution of B-Track model is the analysis of human behaviors directly associated with risk factors and their susceptibility to the development of chronic diseases. The model was evaluated through a prototype, which was used by 10 patients during their treatment. Patients participating in the experiment had habits associated with risk factors with susceptibility to developing coronary heart disease, diabetes, and dementia. Some of these patients already had heart disease, hypertension, or diabetes. Patients P4, P5, P6, P7, and P8 showed positive changes in their behaviors in the long term, where P4 increased their consumption of healthy foods, P5 started exercising with highest frequency, and P6, P7, and P8 also made positive changes to their exercise habits. Patient P1 showed no changes, and the others had only shorter-term improvements. Overall, the TAM evaluation showed that B-Track model was useful to 83% of patients, and 80% of the patients found the model easy to use.
HealCity: Using Citizens’ Vital Sign Data and Elasticity Techniques for Managing Health Environments in Smart Cities
Cidades inteligentes podem melhorar a qualidade de vida dos cidadãos otimizando a utilização de recursos. Em um ambiente conectado à IoT, a saúde das pessoas pode ser monitorada constantemente, o que pode ajudar a identificar problemas médicos antes que se tornem sérios. No entanto, hospitais superlotados podem levar a longos tempos de espera para que os pacientes recebam tratamento. Este trabalho apresenta o HealCity, um modelo focado em cidades inteligentes que pode monitorar o uso de ambientes de saúde pelos pacientes e adaptar a alocação de profissionais de saúde para atender às suas necessidades. O HealCity usa dados de sinais vitais (IoT) em técnicas de previsão para antecipar quando a demanda por um determinado ambiente excederá sua capacidade e sugere ações para alocar profissionais de saúde de acordo. Além disso, introduzimos o conceito de Elasticidade Multinível de Recursos Humanos em Cidades Inteligentes, gerenciando assim os recursos humanos em diferentes níveis de uma cidade inteligente. Um algoritmo também é desenvolvido para gerenciar e identificar automaticamente o hospital apropriado para um possível paciente futuro. O HealCity foi avaliado com quatro ambientes hospitalares e obteve resultados promissores: comparado a hospitais com alocações estática de profissionais, ele reduziu o tempo de espera para atendimento em até 87,62%
Automated pain recognition from facial movements of newborns admitted to Neonatal Intensive Care Units
This thesis aimed to develop an automated method for recognizing pain in neonates using state-of-the-art image processing and artificial intelligence techniques. The method segments facial regions and classifies pain-indicative movements. Analyzed in four datasets, the method showed promising results, with the nasolabial furrow deepening movement standing out as the most reliable region for pain diagnosis. A new scoring method for automatic pain assessment was further proposed and demonstrated high reliability. This new approach optimizes traditional methods, providing a more objective and precise neonatal pain assessment in complex clinical scenarios.
Characterization of Thyroid Tissue through Hyperspectral Image Analysis Using Deep Learning
Thyroid tissue classification is essential for identifying cellular anomalies in biopsies, aiding in the diagnosis of thyroid pathologies. Unlike traditional computer vision approaches, this thesis proposes a novel method based on the interaction of light with the tissue, represented by intensity spectra modeled as hyperspectral images. Six approaches were evaluated, including traditional and deep learning methods, with emphasis on one-dimensional convolutional neural networks, which achieved 97.60% accuracy. The results demonstrate superior efficiency in detecting pathologies compared to conventional analyses, highlighting the potential for advancements in modern pathology.
MultSurv: A Multimodal Deep Learning Model for Hospitalized Patients Survival Analysis in the Context of a Pandemic
Respiratory infectious diseases continue to be a significant public health challenge. For example, the COVID-19 pandemic has exposed critical weaknesses in healthcare systems worldwide. The heterogeneity of clinical manifestations in hospitalized patients highlights the need for risk stratification methods to aid in better patient management and resource allocation. In this study, we propose MultSurv MultSurv, a multimodal survival analysis model that integrates clinical, laboratory, and chest X-ray data to capture the temporal dynamics of disease progression. The methodology comprises five main components: pre-processing, feature encoders, temporal attention, CheXReport for image analysis, and multitask neural networks for risk assessment. The model was evaluated using a private dataset, demonstrating superior performance over state-of-the-art methods, with a C-index of 0.723(t = 1) and 0.695(t = 7). The proposed approach can improve patient prioritization in pandemic scenarios and be adapted for broader clinical applications, potentially contributing to better decision-making in healthcare settings.
Risk situation detector for elderly people based on time series analysis
Elderly people are more exposed to risk situations such as falls, sudden changes in vital signs and fainting. These situations become more common at this stage of life due to the natural decrease in the body's ability to coordinate movements adequately. Numerous studies have proposed health monitoring systems for this population group, but the use of these systems in real situations has shown that this approach is still insufficient to accurately differentiate a risk situation from person's daily activities. This project proposes the development of an effective and reliable health monitoring system for the elderly, through the continuous collection of time series extracted from movement sensors associated with vital signs. For this evaluation, an environment composed of a wearable device simulator, a mobile application simulator and a cloud system simulator was created, very close to the real scenario. This system, in its final model, presented an overall accuracy of 97%, showing that sensor fusion in a continuous data analysis architecture contributes to increasing the elderly risk detection capacity.
Image Processing Techniques Applied to Oral Medicine
This thesis’ main objective is to investigate the applicability of image processing techniques in the oral imaging field, through a selection of oral issues use cases that can be mapped into different computational tasks. In order to investigate the validity of this approach, a series of methods are proposed employing different IP techniques. This work presents a compilation of these different solutions as an unique toolset to be employed in clinical scenarios, assisting oral medicine experts in several tasks with decision-supporting algorithms for diagnosis of oral issues, instead of only providing visualization interfaces for image exams as current commercial solutions.
MEPCA: a technical model to improve on-chain Electronic Health Records processing
Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.
Automating Risk of Bias Inference in Clinical Studies
One of the best quality indicators in the clinical domain is the risk of bias (RoB). Bias refers to any systematic error in results that may lead to misinterpretation. In the systematic review process, human reviewers manually assess the RoB. Existing works attempt to automate this process using support vector machines (SVM), convolutional neural networks (CNN), or logistic regression. To the best of our knowledge, no previous work has explored Transformer-based models for the RoB assessment in clinical studies. In this work, we propose a novel model for RoB inference based on the Transformers architecture, called RoBIn (i.e., Risk of Bias Inference). We employ a machine reading comprehension (MRC) approach to extract evidence that is then classified with a RoB label. Furthermore, we use distant supervision to annotate a dataset for MRC and RoB inference. As a final contribution, a large language model (LLM) application was created to receive clinical trials as input and to assess the RoB. The proposed model outperforms state-of-the-art approaches and other LLMs in many settings, with high accuracy (AUC-ROC= 0.83) for different bias types.
Reduced mathematical-computational model for action potential, calcium dynamics, and active tension in cardiomyocytes
The simulation of the heart’s electromechanical activity is essential for analyzing cardiac phenomena, but computational models can be costly. In this thesis, a reduced cellular-level model was developed, capable of qualitatively reproducing the heart’s electromechanical activity, considering the electrical, calcium, and mechanical interactions, as well as simulating the effects of different drugs. The model was constructed by coupling the minimal Bueno-Orovio model for the electrical component and simplified equations for the calcium and active tension dynamics, with parameters adjusted by a genetic algorithm. The results showed that the model adequately reproduced the action potential, calcium, and active force with only six ordinary differential equations. Furthermore, in 3D simulations, the developed model showed a significant reduction in computational cost compared to the reference model, with noticeable speed-up gains on both GPU and CPU. The speed-up reached up to 5.75 on the CPU, highlighting the efficiency of the proposed model for extensive biventricular simulations.
Online Skills Evaluation in Regional Anesthesia Training Using a Virtual Reality Simulator
Competency-based learning balances theory and practice, essential in spinal anesthesia and requires psychomotor skills acquired through practice. This study presents AnesteSIM, a virtual reality simulator that covers all stages of spinal anesthesia with haptic feedback and automatic assessment. User actions are evaluated based on literature guidelines and clinical practices, generating detailed performance reports. AnesteSIM is a complementary tool to traditional spinal anesthesia training, offering more safe and interactive training opportunities.
A semantic interoperability model based on NLP for non-structured health data
The increasing volume of unstructured clinical data challenges interoperability, especially in decentralized systems like SUS. This study proposes a semantic model integrating NLP, machine learning, and ontologies to extract and standardize clinical notes. Using Named Entity Recognition (NER), lexical normalization, and alignment with HL7 FHIR and openEHR, the model was tested on COVID-19 data from partner hospitals, demonstrating its effectiveness in structuring unstructured data and enabling scalable interoperability.
Data on Nutrition For You: An app for monitoring patients undergoing nutritional therapy
Hospital malnutrition worsens patient outcomes and increases costs. In Brazil, 30 to 50% of hospitalized patients are or will become malnourished during their hospital stay. Electronic medical records do not effectively address this problem. DataOnNutrition For You was designed to simplify nutritional therapy management, helping nutritionists and dietitians create tailored nutritional prescriptions and track daily infusion. The initial version, available in app stores, allows for precise anthropometric calculations and automated calculation of caloric and protein needs.
DoN-DataOnNutrition®: A platform for managing nutritional therapy in critical patients and supporting ICU research
Nutritional therapy (NT) in critically ill patients remains a challenge. Hospital malnutrition worsens the condition and negatively impacts patients' health. Information technology (IT) and artificial intelligence (AI) offer promising solutions to optimize NT. The DoN-DataOnNutriton® system integrates clinical and laboratory data, providing real-time recommendations and alerts for risks, non-conformities, and changes in tests. The system's structured database, maintained securely and privately in the cloud, facilitates research and the development of personalized solutions to improve precision nutritional therapy.
IoT Software System for Biosafety Monitoring of Facilities
This article presents the SAFE IoT system, a low-cost solution for easily monitoring biosafety indicators in different installation profiles, such as the number of people, CO2 levels, humidity, and temperature. The collected information is displayed on a dashboard, facilitating analysis and decision-making. The system has been evaluated, and the appropriate data transmission frequency is expected to contribute to effective indoor air quality monitoring.
Cardio Surgery Illustrator: A Tool for Cardiovascular Learning and Assistance
The purpose of this paper is to establish, in the health sector, a connection between students and patients interested in improving cardiovascular health through a mobile system developed for the Android platform. Among the implemented functionalities, the following stand out: the integration of multimedia and clickable content for dynamic learning, the incorporation of Large-Scale Language Models (LLMs) using the RASA framework for the construction of an intelligent chatbot, and quizzes to reinforce theoretical knowledge.
e-TesteDoPezinho: Online System for Exam Result Consultation
The heel prick test, performed in the first days of a baby's life, detects important neonatal diseases that, if treated and monitored early, guarantee a better quality of life for the child. Making the tests available at collection sites speeds up the actions of health professionals in case of abnormal screening results. Considering this context, the Neonatal Screening Reference Service of the State University of Campinas (Unicamp), developed an online system to make the heel prick test available in a practical and safe manner to all collection sites within the scope of this service.
Implementation of Modular Architecture for Classifier Integration in the CitoFocus App
The article presents the integration of a classifier based on the YOLO algorithm into the CitoFocus application, which assists cytopathologists in analyzing cytological exams. To ensure scalability and simplified maintenance, the back-end was redesigned using NestJS, a hexagonal architecture, and MVC. The new structure enabled the incorporation of the classifier with minimal impact on the functionality of CitoFocus, enhancing support for professionals and contributing to the reduction of diagnostic errors in Pap smear exams.
RoBIn Chatbot: Leveraging LLMs for Automated Risk of Bias Assessment in Clinical Studies
The Risk of Bias (RoB) assessment is an essential instrument for evaluating the reliability of clinical studies and identifying any systematic error that can occur. This task is traditionally performed by humans, and only a few works tried to automate it using machine learning. Recent advances in large language models (LLMs) have revolutionized natural language processing and information retrieval, allowing us to build applications that can chat with documents and perform the most diverse tasks. In this work, we propose RoBIn chatbot, an LLM application able to receive clinical studies as input and classify their RoB. RoBIn chatbot uses a model trained on data derived from the Cochrane Database of Systematic Reviews and is able to perform inference for six bias types. To prevent the LLM from generating misleading conclusions, it relies on retrieval-augmented generation on the submitted file to extract the piece of evidence and send it to a pretrained model responsible for performing RoB inference.
An mHealth Application for Home-Based Palliative Care
Palliative care aims to improve the quality of life for patients with life-threatening illnesses. This study presents the mHealth Palliative Control application extended for home care via telehealth. The Palliative Control application has been extended for utilization in home care via telehealth. This novel iteration has been developed to empower patients and caregivers by enabling them to record symptoms and access educational content, conferring heightened autonomy to patients, family members, and health professionals. Additionally, a blockchain-based security module is under development to ensure the integrity and privacy of patient data. The system is currently undergoing validation with the Hospital das Clínicas (HC) of the Federal University of Goiás (UFG), with testing planned after the full implementation of the proposed architecture.
SimulaPed: A Serious Game for Pediatric and Neonatal Nursing Medication Dosage
This article presents the development of SimulaPed, a serious game designed to assist in teaching safety in medication administration in pediatric and neonatal nursing. The game proposes fictional clinical scenarios in which the player navigates through clinical cases and questions, receiving immediate feedback on correct answers and the consequences of errors. The efficacy evaluation was conducted using an initial prototype and sent to various professionals in the field, with preliminary results indicating that the game is a promising tool for enhancing students’ learning and confidence in the safe administration of medications.
SobreVidas Platform – “Oral Cancer”: A Technological Solution for Tracking and Continuous Monitoring in Primary Health Care
Oral cancer has a high mortality rate, especially when diagnosed at advanced stages. This paper presents the SobreVidas – “Oral Cancer” Platform, an artificial intelligence-based solution designed to support the screening and monitoring of at-risk populations in Primary Health Care. By applying agile methods for software management and development, a microservices architecture, authentication via Keycloak, large language models, and Retrieval-Augmented Generation techniques, the platform is secure, scalable, and focused on clinical decision support. The results indicate technical feasibility and strong potential to enhance early detection and the follow-up of individuals at risk.
Machine Learning in Health: A Systematic Review
The aim of this study is to construct a taxonomy of Machine Learning (ML) techniques most commonly employed in health applications. The systematic review was performed between October 2023 and November 2024, and comprised articles published from 2013 to 2023. The search for relevant references was done on Pubmed and MEDLINE databases, and the screening articles were analysed by two reviewers independently through the Covidence platform. We identified 2,714 articles resulting in 65 health problems related to some ML technique and 8 types of learning. This research brings trends in use of ML for application in the field of medicine in general.
Neuropsychiatric Disorders Classification using EEG Signal and Deep Neural Networks
Electroencephalogram (EEG) is widely used to measure brain activity and serves as a promising tool in the diagnosis of neuropsychiatric disorders such as anxiety, schizophrenia, and epilepsy, among others. We proposed a deep neural network model to classify neuropsychiatric conditions using EEG signals. The data was processed with PSD and FC features using SMOTE method to handle with unbalanced classes and dimensionality reduction using PCA. The model achieved an accuracy of 71.43% and a mean AUC of 0.9383, with better performance in the obsessive compulsive and anxiety disorders classification. At the same time, mood disorder showed to be more challenging in the classification.
Ergonomic Assessment Using Human Pose Estimation: A Real-Time Approach with YOLO and BlazePose
Musculoskeletal disorders (MSDs) are a major cause of workplace injuries, often linked to poor posture and repetitive movements. Traditional assessments like RULA are subjective and labor-intensive. To overcome these limitations, this study employs computer vision for automated ergonomic evaluation. Using YOLO for person detection and BlazePose for pose estimation, the system analyzes workplace postures via surveillance cameras, generating RULA scores to assess ergonomic risks. Tested on the MPI-INF-3DHP dataset, it achieves MPJPE-PA values between 243.91 mm and 295.70 mm. This scalable, objective approach enhances workplace safety. Future work will refine accuracy, handle real-world variations, and integrate real-time feedback.
Supporting Stroke Rehabilitation Through Gamified Daily Activity Simulations
This paper introduces the creation of an open-source virtual reality (VR) game for stroke rehabilitation by simulating daily activities. The game proposes immersive environments, including a tea room, bathroom, garden, and kitchen, designed to promote functional gesture movements and enhance user engagement by introducing gamification. Patients are rewarded with points for task completion, for example, grabbing a fruit and eating, thus creating a competitive and motivating environment. Preliminary testing with university students received positive responses, particularly regarding the realism of the tasks. However, extended use of VR resulted in reports of physical discomfort and headaches. Next steps include the approval for further testing by the Ethics Committee involving stroke patients.
Clinical validation of an artificial intelligence model for tuberculosis detection in microbiologically confirmed chest x-rays
This study presents an artificial intelligence (AI) algorithm based on a ResNet-50 architecture designed to identify chest X-rays with patterns suggestive of pulmonary tuberculosis (TB). The model was trained and internally validated on 13,023 images from public datasets and then clinically validated using a unique dataset of radiographs from patients with microbiologically confirmed TB, with assessments performed by a thoracic radiologist. On public data, the algorithm achieved an AUC of 0.89, sensitivity of 0.84, and specificity of 0.72, while clinical validation yielded an AUC and sensitivity of 1.00. These results underscore the critical importance of incorporating microbiological confirmation as the gold standard to ensure the accuracy and clinical reliability of AI models in TB detection.
Vision Scan Insight: An Intelligent Assistant Using Deep Neural Networks for Low Vision Users in Supermarkets
This article proposes an intelligent tool to assist people with blindness or low vision during shopping visits in a supermarket. The solution utilizes deep neural networks (CNNs) to detect objects, integrated into an application that offers real-time auditory feedback. The system integrates three YOLO variants (v5, v8, and v9), each retrained using both full fine-tuning and restricted transfer learning on three product datasets (Food, mAP50−95 = 0.683; No-Fridge, mAP50−95 = 0.697; Groceries, mAP50−95 = 0.916).
AI and Accessibility: Developing an App to Adapt Texts and Interfaces for Neurodivergent Inclusion
This paper presents the development of an application based on artificial intelligence (AI)for adapting texts and digital interfaces, aiming to promote accessibility for neurodivergent individuals, such as those with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). The application leverages AI to simplify texts and improve reading and digital interaction experiences. Preliminary results indicate that the proposed approach significantly enhances text comprehension, contributing to the digital inclusion of neurodivergent individuals.
Pairwise Difference Filter (PDF): An Interpretable Preprocessing Method for Medical and Beyond
Interpretability is a critical requirement for machine learning models in healthcare, as clinicians need to understand how the input features are processed to make informed decisions about patient care. Traditional preprocessing methods often fail to capture subtle differences between patient groups, particularly in datasets with overlapping or highly similar classes. To address these challenges, we propose Pairwise Difference Filter (PDF), a novel pre-processing method that leverages pairwise differences between samples of opposite classes to identify the most influential features. PDF focuses on pairs of patients with the smallest overall differences but significant differences in specific features, enabling the identification of clinically meaningful biomarkers. By enhancing the interpretability of machine learning models, PDF supports medical decision-making and improves the transparency of predictive models in healthcare. Experimental results with three different on a COVID-19 severity classification dataset, MUSIC (a dataset for predicting outcomes in patients with several degrees of heart failure) and Wine Toy Dataset demonstrate that PDF achieves competitive performance while providing interpretable feature rankings that align with clinical knowledge.