Interactive Information Visualization as Support for Computer-Aided Diagnosis: Prototype and Qualitative Evaluation
Resumo
Research Context: Medical Information Systems (MIS) have evolved significantly to achieve efficiency and scalability in processing large amounts of multimodal data. Computer-Aided Diagnosis (CAD) models offer a second opinion to help physicians compose a more precise diagnosis. Effective CAD models need to be built on diverse, multicentric and, in specific cases, multimodal databases. Scientific and/or Practical Problem: The lack of standardization and complex interoperability among different MIS are major challenges that hinder the exploration of the maximum potential of digital data in healthcare applications, including the development of CAD models. Proposed Solution and/or Analysis: A generic, flexible and reusable relational data model was applied, developed specifically to support the training of CAD models from multimodal data. Through a case study using Cardiac Magnetic Resonance exams, a prototype with interactive Information Visualization (IV) functionalities was developed and qualitatively evaluated. Related IS Theory: The evaluation was inspired by the Fit-Viability Theory, as the prototype was evaluated primarily with regard to its potential for effective integration into real-world clinical workflows. Research Method: The research included: literature review; modeling; prototyping; development; qualitative evaluation through interviews with health professionals; and analysis and summarization of results. Summary of Results: Evaluated IV tools demonstrated strong potential to enhance clinical decision-making for the interviewed health professionals. From a Fit-Viability perspective, the system showed a promising fit with the users’ analytical needs and clinical workflows, while also indicating viability in terms of technical feasibility and resource compatibility within the healthcare setting. Contributions and Impact to IS area: Results showed that the strategic combination of a flexible data model and customizable IV functionalities can provide valuable research tools and diagnostic aids to physicians. These insights offer practical guidance for enhancing MIS and CAD capabilities, particularly in supporting the effective analysis of medical databases.
Referências
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Nascimento, D. C., Vanderlei, I. M., and Silva, D. d. (2025). Conceptual modeling of algorithm parameterization and results considering volatile data requirements: A case study in the context of primary healthcare. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 399–408, Porto Alegre. SBC.
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Polychronidou, E., Kalamaras, I., Votis, K., and Tzovaras, D. (2019). Health Vision: an interactive web based platform for healthcare data analysis and visualisation. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1–8. IEEE.
Poppe, O., Sæbø, J. I., and Braa, J. (2019). Strategies for standardizing health information analysis. In Nielsen, P. and Kimaro, H. C., editors, Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D, pages 260–271, Cham. Springer International Publishing.
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Wickramasinghe, N., Schaffer, J. L., Seitz, J., Muhammad, I., and Vogel, D. (2018). Fitviability model examination of e-health solutions. In Wickramasinghe, N. and Schaffer, J. L., editors, Theories to Inform Superior Health Informatics Research and Practice, pages 251–269. Springer International Publishing.
Wilkin, C., Ferreira, A., Rotaru, K., and Gaerlan, L. R. (2020). Big data prioritization in scm decision-making: its role and performance implications. International Journal of Accounting Information Systems, 38:100470.
Wise Coders GmbH (2023). DbSchema – database design, management & collaboration tool. Available at: [link]. Accessed: Feb. 23th, 2025.
Yeshchenko, A. and Mendling, J. (2024). A survey of approaches for event sequence analysis and visualization. Information Systems, 120(102283):1–20.
Younas, A., Malik, M. S. A., ur Rehman, K., and Shahid, R. (2016). A detailed study on temporal data visualization techniques in electronic health records. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pages 638–643. IEEE.
Zhao, Y., Yu, Y., Li, Y., Han, G., and Du, X. (2019). Machine learning based privacy-preserving fair data trading in big data market. Information Sciences, 478:449–460.
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Almeida, C., Bernardino, H., Souza, J., and Campos, L. (2025). Real-time heart failure prediction: An approach for ambient assisted living. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação, pages 57–66, Porto Alegre, RS, Brasil. SBC.
Almeida, J. R., Pazos, A., and Oliveira, J. L. (2023). Clinical data integration strategies for multicenter studies. In DoCEIS 2023: Technological Innovation for Digital Transformation, pages 175–190. Springer.
Alvim, L. T., Gonçalves, V. M., and Nunes, F. L. S. (2025). Building flexible databases by using web services for computer-aided diagnosis of cardiomyopathies: from conceptual definition to evaluation. Accepted for publication on the Journal of the Brazilian Computer Society.
Bandeira, J. d. S. and Fagundes, R. A. d. A. (2025). Enhancing Alzheimer’s disease diagnosis: Insights from MLP and 1D CNN models. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 359–368, Porto Alegre. SBC.
Beerkens, M. (2022). An evolution of performance data in higher education governance: a path towards a ‘big data’ era? Quality in Higher Education, 28(1):29–49.
Bergamasco, L. C. C., Lima, K. R. P. S., Rochitte, C. E., and Nunes, F. L. S. (2022). A bipartite graph approach to retrieve similar 3D models with different resolution and types of cardiomyopathies. Expert Systems with Applications, 193(116422):1–18.
Carlisle, S. (2018). Software: Tableau and Microsoft Power BI. Technology | Architecture + Design, 2(2):256–259.
Cheng, L. and Senathirajah, Y. (2023). Using clinical data visualizations in electronic health record user interfaces to enhance medical student diagnostic reasoning: randomized experiment. JMIR Human Factors, 10(e38941):1–9.
Costa, S. S. H., Gonçalves, V. M., and Nunes, F. L. S. (2024). Applying ventricular wall shape and motion features from CMRI for aiding diagnosis of cardiomyopathies. In Proceedings of the 24th Brazilian Symposium on Computing Applied to Health, pages 142–153. SBC.
Delmondes, P. H. M., Bergamasco, L. C. C., and Nunes, F. L. S. (2015). Recuperação de modelos médicos por conteúdo usando extrator local de área e distância. Revista de Informática Teórica e Aplicada, 22(2):10–30.
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4):198–211.
Federative Republic Brazil (2018). Law no. 13,709 of August 14th, 2018. Available at: [link]. Accessed: Feb. 2nd, 2025. General Law on Personal Data Protection (LGPD).
Gonçalves, V. M., Bergamasco, L. C. C., and Nunes, F. L. S. (2024). 3D Hough Transform-based left ventricle 3D object classification for cardiomyopathy diagnosis. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1–6. IEEE.
Graciano Neto, V. V., Oquendo, F., and Nakagawa, E. Y. (2017). Smart systems-of-information systems: Foundations and an assessment model for research development. In I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016 – 2026, chapter 2. Sociedade Brasileira de Computação, Porto Alegre.
Gómez-Flores, W., Gregorio-Calas, M. J., and Coelho de Albuquerque Pereira, W. (2024). BUS-BRA: a breast ultrasound dataset for assessing computer-aided diagnosis systems. Medical Physics, 51(4):3110–3123.
Horne, E., Tibble, H., Sheikh, A., and Tsanas, A. (2020). Challenges of clustering multi-modal clinical data: review of applications in asthma subtyping. JMIR Medical Informatics, 8(5):e16452:1–e16452:20.
Huesch, M. D. and Mosher, T. J. (2017). Using it or losing it? the case for data scientists inside health care. Catalyst Carryover, 3(3).
Kaseb, M. R., Khafagy, M. H., Ali, I. A., and Saad, E. M. (2019). An improved technique for increasing availability in big data replication. Future Generation Computer Systems, 91:493–505.
Lin, C., Sun, Q., and Wang, C. (2019). Web-based management and visualization of myocardial ischemia data. In 2019 Chinese Automation Congress (CAC), pages 5424–5428. IEEE.
Lopes, F. S., Silva, L. A. d., and Breternitz, V. J. (2017). Research and education in Data Science: Challenges for the area of Information Systems. In I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016 – 2026, chapter 14. Sociedade Brasileira de Computação, Porto Alegre.
Maciel, R. S. P., David, J. M. N., Claro, D. B., and Braga, R. (2017). Full interoperability: Challenges and opportunities for future information systems. In I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016 – 2026, chapter 9. Sociedade Brasileira de Computação, Porto Alegre.
Malik, M. S. A. and Sulaiman, S. (2013). Towards the development of an interface model for information visualization in multiple electronic health records. In 2013 International Conference on Computer Medical Applications (ICCMA), pages 1–5. IEEE.
Mandell, G. A., Keating, M. B., and Khayal, I. S. (2022). Development of a visualization tool for healthcare decision-making using electronic medical records: a systems approach to viewing a patient record. In 2022 IEEE International Systems Conference (SysCon), pages 1–8. IEEE.
Marques, B. T., Magalhães, R. P., Almada, L., do V. Madeiro, J. P., Mattos, C. L. C., and Macedo, J. (2025). Interpretability of machine learning models for cardiac arrhythmia classification. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 489–496, Porto Alegre. SBC.
Mettler, T. (2016). Anticipating mismatches of hit investments: Developing a viability-fit model for e-health services. International Journal of Medical Informatics, 85(1):104– 115.
Muskan, Singh, G., Singh, J., and Prabha, C. (2022). Data visualization and its key fundamentals: a comprehensive survey. In 2022 7th International Conference on Communication and Electronics Systems (ICCES), pages 1710–1714. IEEE.
Nascimento, D. C., Vanderlei, I. M., and Silva, D. d. (2025). Conceptual modeling of algorithm parameterization and results considering volatile data requirements: A case study in the context of primary healthcare. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI), pages 399–408, Porto Alegre. SBC.
Pallets (2024). Welcome to Flask – Flask Documentation (3.1.x). Available at: [link]. Accessed: Feb. 23th, 2025.
Polychronidou, E., Kalamaras, I., Votis, K., and Tzovaras, D. (2019). Health Vision: an interactive web based platform for healthcare data analysis and visualisation. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pages 1–8. IEEE.
Poppe, O., Sæbø, J. I., and Braa, J. (2019). Strategies for standardizing health information analysis. In Nielsen, P. and Kimaro, H. C., editors, Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D, pages 260–271, Cham. Springer International Publishing.
Rank, N., Pfahringer, B., Kempfert, J., Stamm, C., Kühne, T., Schoenrath, F., Falk, V., Eickhoff, C., and Meyer, A. (2020). Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. npj Digital Medicine, 3(139):1–12.
Ruan, W., Appasani, N., Kim, K., Vincelli, J., Kim, H., and Lee, W.-S. (2018). Pictorial visualization of EMR summary interface and medical information extraction of clinical notes. In 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pages 1–6. IEEE.
Salmi, M., Atif, D., Oliva, D., Abraham, A., and Ventura, S. (2024). Handling imbalanced medical datasets: review of a decade of research. Artificial Intelligence Review, 57(273).
Sharma, K., Shetty, A., Jain, A., and Dhanare, R. K. (2021). A comparative analysis on various business intelligence (BI), data science and data analytics tools. In 2021 International Conference on Computer Communication and Informatics (ICCCI), pages 1–11. IEEE.
Silva, L. S., Aranha, R. V., Ribeiro, M. A. O., Nakamura, R., and Nunes, F. L. S. (2020). Exploring visual attention and machine learning in 3D visualization of medical temporal data. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pages 146–151. IEEE.
Simmons, C. (2013). WebPAX PACS and Universal Viewer v8 Medical Imaging System announced by Heart IT. Available at: [link]. Accessed: Feb. 9th, 2025.
The PostgreSQL Global Development Group (2023). PostgreSQL: the world’s most advanced open source database. Available at: [link]. Accessed: Feb. 23th, 2025.
Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., and Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: a systematic literature review. BMC Medical Informatics and Decision Making, 23(318).
Veriscimo, E. d. S., Bernardes Junior, J. L., and Digiampietri, L. A. (2020). Evaluating user experience in 3d interaction: a systematic review. In Proceedings of the XVI Brazilian Symposium on Information Systems, SBSI ’20, New York, NY, USA. Association for Computing Machinery.
Vieira, J. M. L. and Correa, R. F. (2011). Visualização da informação na construção de interfaces amigáveis para sistemas de recuperação de informação. Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação, 16(32):73–93.
Viglio, S., Rodrigues, P., Jawabri, O. A., Espindola, V., Figueredo, L., Bulcão-Neto, R., Ribeiro-Rotta, R., and Neto, V. G. (2025). A systematic mapping on software functionalities in information systems used by community health agents for mouth cancer prevention. In Anais do XXI Simpósio Brasileiro de Sistemas de Informação, pages 39–48, Porto Alegre, RS, Brasil. SBC.
Wang, S., Zhao, Z., Ouyang, X., Wang, Q., and Shen, D. (2024). Interactive computer-aided diagnosis on medical image using large language models. Communications Engineering, 3(133):1–9.
Wickramasinghe, N., Schaffer, J. L., Seitz, J., Muhammad, I., and Vogel, D. (2018). Fitviability model examination of e-health solutions. In Wickramasinghe, N. and Schaffer, J. L., editors, Theories to Inform Superior Health Informatics Research and Practice, pages 251–269. Springer International Publishing.
Wilkin, C., Ferreira, A., Rotaru, K., and Gaerlan, L. R. (2020). Big data prioritization in scm decision-making: its role and performance implications. International Journal of Accounting Information Systems, 38:100470.
Wise Coders GmbH (2023). DbSchema – database design, management & collaboration tool. Available at: [link]. Accessed: Feb. 23th, 2025.
Yeshchenko, A. and Mendling, J. (2024). A survey of approaches for event sequence analysis and visualization. Information Systems, 120(102283):1–20.
Younas, A., Malik, M. S. A., ur Rehman, K., and Shahid, R. (2016). A detailed study on temporal data visualization techniques in electronic health records. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pages 638–643. IEEE.
Zhao, Y., Yu, Y., Li, Y., Han, G., and Du, X. (2019). Machine learning based privacy-preserving fair data trading in big data market. Information Sciences, 478:449–460.
Zhou, W., Yang, Y., Yu, C., Liu, J., Duan, X., Weng, Z., Chen, D., Liang, Q., Fang, Q., Zhou, J., Ju, H., Luo, Z., Guo, W., Ma, X., Xie, X., Wang, R., and Zhou, L. (2021). Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images. Nature Communications, 12(1259):1–14.
Zhu, C. (2019). Big data as a governance mechanism. The Review of Financial Studies, 32(5):2021–2061.
Publicado
25/05/2026
Como Citar
ALVIM, Larissa Terto; GONÇALVES, Vagner Mendonça; RIBEIRO, Matheus A. O.; NUNES, Fátima L. S..
Interactive Information Visualization as Support for Computer-Aided Diagnosis: Prototype and Qualitative Evaluation. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 693-712.
DOI: https://doi.org/10.5753/sbsi.2026.248598.
