Validação clínica de modelo de inteligência artificial para detecção de tuberculose em radiografias de tórax em casos microbiologicamente confirmados
Resumo
Este trabalho apresenta um algoritmo de inteligência artificial (IA) baseado na arquitetura ResNet-50, desenvolvido para identificar radiografias de tórax com padrões sugestivos de tuberculose (TB). O modelo foi treinado e validado internamente com 13.023 imagens de bases públicas e, posteriormente, validado clinicamente utilizando um conjunto exclusivo de radiografias de pacientes com TB confirmada microbiologicamente e revisadas por radiologista torácico. Nos dados públicos, o algoritmo obteve AUC de 0,89, sensibilidade de 0,84 e especificidade de 0,72, enquanto a validação clínica apresentou AUC e sensibilidade de 1,00. Esses resultados ressaltam a importância crucial de utilizar a confirmação microbiológica como padrão-ouro para garantir a precisão e a confiabilidade clínica dos modelos de IA na detecção da TB.Referências
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Del Ciello, A., Franchi, P., Contegiacomo, A., et al. (2017). Missed lung cancer: when, where, and why? Diagnostic and interventional radiology, 23(2):118.
Harris, M., Qi, A., Jeagal, L., et al. (2019). A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PloS one, 14(9):e0221339.
Huy, V. T. Q. and Lin, C.-M. (2023). An improved densenet deep neural network model for tuberculosis detection using chest x-ray images. IEEE Access, 11:42839–42849.
Irvin, J., Rajpurkar, P., Ko, M., et al. (2019). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 590–597.
Jaeger, S., Candemir, S., Antani, S., Wang, Y.-X. J., Lu, P.-X., and Thoma, G. (2014). Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery, 4(6):475.
Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., and Park, S. H. (2019). Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean journal of radiology, 20(3):405–410.
Malik, H., Anees, T., Din, M., and Naeem, A. (2023). Cdc net: Multi-classification convolutional neural network model for detection of covid-19, pneumothorax, pneumonia, lung cancer, and tuberculosis using chest x-rays. Multimedia Tools and Applications, 82(9):13855–13880.
Ministério da Saúde (MS) (2024). Brasil avança na prevenção, diagnóstico e tratamento da tuberculose. Acesso em: 24 fev. 2025.
Mungai, B., Ong ‘angò, J., Ku, C. C., et al. (2022). Accuracy of computer-aided chest x-ray in community-based tuberculosis screening: Lessons from the 2016 kenya national tuberculosis prevalence survey. PLOS global public health, 2(11):e0001272.
Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., et al. (2022). Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations. Scientific Data, 9(1):429.
Organização Mundial da Saúde (OMS) (2024). Global Tuberculosis Report 2024. WHO, Geneva. Acesso em: 24 fev. 2025.
Organização Pan-Americana da Saúde (OPAS) (2024). Opas pede que as américas adotem tecnologias e tratamentos inovadores para eliminar a tb. Acesso em: 24 fev. 2025.
Park, S. H. and Kressel, H. Y. (2018). Connecting technological innovation in artificial intelligence to real-world medical practice through rigorous clinical validation: what peer-reviewed medical journals could do. Journal of Korean Medical Science, 33(22):e152.
Qin, Z. Z., Ahmed, S., Sarker, M. S., Paul, K., et al. (2021). Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. The Lancet Digital Health, 3(9):e543–e554.
Reis, E. P., de Paiva, J. P. Q., da Silva, M. C. B., Ribeiro, G. A. S., et al. (2022). Brax, brazilian labeled chest x-ray dataset. Scientific Data, 9(1):487.
Rosenthal, A., Gabrielian, A., Engle, E., et al. (2017). The tb portals: an open-access, web-based platform for global drug-resistant-tuberculosis data sharing and analysis. Am Soc Microbiol, 55(11):3267–3282.
Tack, D. and Howarth, N. (2019). Missed lung lesions: side-by-side comparison of chest radiography with mdct. Diseases of the Chest, Breast, Heart and Vessels 2019-2022: Diagnostic and Interventional Imaging, pages 17–26.
Yang, Y., Xia, L., Liu, P., Yang, F., Wu, Y., Pan, H., Hou, D., Liu, N., and Lu, S. (2023). A prospective multicenter clinical research study validating the effectiveness and safety of a chest x-ray-based pulmonary tuberculosis screening software jf cxr-1 built on a convolutional neural network algorithm. Frontiers in Medicine, 10:1195451.
Cohen, J. P., Viviano, J. D., Bertin, P., et al. (2022). Torchxrayvision: A library of chest x-ray datasets and models. In International Conference on Medical Imaging with Deep Learning, pages 231–249. PMLR.
de Camargo, T. F. O., Ribeiro, G. A. S., da Silva, M. C. B., et al. (2025). Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs. Frontiers in Artificial Intelligence, 8:1512910.
Del Ciello, A., Franchi, P., Contegiacomo, A., et al. (2017). Missed lung cancer: when, where, and why? Diagnostic and interventional radiology, 23(2):118.
Harris, M., Qi, A., Jeagal, L., et al. (2019). A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PloS one, 14(9):e0221339.
Huy, V. T. Q. and Lin, C.-M. (2023). An improved densenet deep neural network model for tuberculosis detection using chest x-ray images. IEEE Access, 11:42839–42849.
Irvin, J., Rajpurkar, P., Ko, M., et al. (2019). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 590–597.
Jaeger, S., Candemir, S., Antani, S., Wang, Y.-X. J., Lu, P.-X., and Thoma, G. (2014). Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery, 4(6):475.
Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., and Park, S. H. (2019). Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean journal of radiology, 20(3):405–410.
Malik, H., Anees, T., Din, M., and Naeem, A. (2023). Cdc net: Multi-classification convolutional neural network model for detection of covid-19, pneumothorax, pneumonia, lung cancer, and tuberculosis using chest x-rays. Multimedia Tools and Applications, 82(9):13855–13880.
Ministério da Saúde (MS) (2024). Brasil avança na prevenção, diagnóstico e tratamento da tuberculose. Acesso em: 24 fev. 2025.
Mungai, B., Ong ‘angò, J., Ku, C. C., et al. (2022). Accuracy of computer-aided chest x-ray in community-based tuberculosis screening: Lessons from the 2016 kenya national tuberculosis prevalence survey. PLOS global public health, 2(11):e0001272.
Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., et al. (2022). Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations. Scientific Data, 9(1):429.
Organização Mundial da Saúde (OMS) (2024). Global Tuberculosis Report 2024. WHO, Geneva. Acesso em: 24 fev. 2025.
Organização Pan-Americana da Saúde (OPAS) (2024). Opas pede que as américas adotem tecnologias e tratamentos inovadores para eliminar a tb. Acesso em: 24 fev. 2025.
Park, S. H. and Kressel, H. Y. (2018). Connecting technological innovation in artificial intelligence to real-world medical practice through rigorous clinical validation: what peer-reviewed medical journals could do. Journal of Korean Medical Science, 33(22):e152.
Qin, Z. Z., Ahmed, S., Sarker, M. S., Paul, K., et al. (2021). Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. The Lancet Digital Health, 3(9):e543–e554.
Reis, E. P., de Paiva, J. P. Q., da Silva, M. C. B., Ribeiro, G. A. S., et al. (2022). Brax, brazilian labeled chest x-ray dataset. Scientific Data, 9(1):487.
Rosenthal, A., Gabrielian, A., Engle, E., et al. (2017). The tb portals: an open-access, web-based platform for global drug-resistant-tuberculosis data sharing and analysis. Am Soc Microbiol, 55(11):3267–3282.
Tack, D. and Howarth, N. (2019). Missed lung lesions: side-by-side comparison of chest radiography with mdct. Diseases of the Chest, Breast, Heart and Vessels 2019-2022: Diagnostic and Interventional Imaging, pages 17–26.
Yang, Y., Xia, L., Liu, P., Yang, F., Wu, Y., Pan, H., Hou, D., Liu, N., and Lu, S. (2023). A prospective multicenter clinical research study validating the effectiveness and safety of a chest x-ray-based pulmonary tuberculosis screening software jf cxr-1 built on a convolutional neural network algorithm. Frontiers in Medicine, 10:1195451.
Publicado
09/06/2025
Como Citar
SILVA, Maria C. B. da; CAMARGO, Thiago F. O. de; PAIVA, Joselisa P. Q. de; HORVATH, Jaqueline D. C.; RIBEIRO, Guilherme A. S..
Validação clínica de modelo de inteligência artificial para detecção de tuberculose em radiografias de tórax em casos microbiologicamente confirmados. In: TECNOLOGIAS ASSISTIVAS, INTELIGÊNCIA ARTIFICIAL E CIÊNCIA DE DADOS - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 305-311.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2025.7025.
