Application of artificial intelligence techniques for the detection of pulmonary tuberculosis in Colombia

Aplicación de técnicas de inteligencia artificial para la detección de tuberculosis pulmonar en Colombia

Published 2022-12-20

Red neuronal para la detección de tuberculosis pulmonar
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Vol. 20 No. 39 (2023): Table of contents Revista EIA No. 39
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Application of artificial intelligence techniques for the detection of pulmonary tuberculosis in Colombia. (2022). Revista EIA, 20(39), 3909. pp. 1-23. https://doi.org/10.24050/reia.v20i39.1617
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https://doi.org/10.24050/reia.v20i39.1617
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Oscar Fernando Bedoya Leiva
Universidad del Valle, Colombia.
https://orcid.org/0000-0002-9378-7337
Harry Santiago Guarín Aristizábal
Universidad del Valle, Colombia.
Jared Zayiri Agudelo Delgado
Universidad Libre, Colombia.
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Tuberculosis is a respiratory disease that affects lungs and it is caused by the bacillus Mycobacterium tuberculosis (MTB), which is spread when people who are sick with tuberculosis expel bacteria into the air by coughing. Before the coronavirus (COVID-19) pandemic, tuberculosis was the leading cause of death from infectious agents even ranking above HIV/AIDS. Growing MTB on solid medium is the main diagnostic reference method. In addition, this is the standard method for identifying the bacterial resistance profile. However, the waiting period for the results is long, during which time a patient can be highly contagious. In turn, delaying treatment for tuberculosis can increase disease severity, but treating without diagnostic confirmation can lead to bacterial resistance, a higher rate of adverse events, and higher costs. Therefore, pulmonary tuberculosis disease must be rapidly diagnosed by a cost-effective method. This paper proposes models for the detection of pulmonary tuberculosis by using different artificial intelligence techniques. These models can be used to support decision making of doctors and are intended to identify whether a patient has tuberculosis or not. In particular, four supervised learning techniques are used (neural networks, decision trees, and two ensemble methods). Each model allows predicting a positive or negative diagnosis of pulmonary tuberculosis based on previously recorded diagnostic variables taken from patients in Cali, Colombia. According to the results, the Extra Trees method reaches the highest accuracy compared to the other techniques used for the prediction of pulmonary tuberculosis with an area under the ROC curve of 95.63%.

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  1. Evangelista, L.; Guedes, E. (2019). Ensembles of Convolutional Neural Networks on Computer-Aided Pulmonary Tuberculosis Detection. IEEE Latin America Transactions, 17(12), pp. 1954-1963. https://doi.org/10.1109/TLA.2019.9011539
  2. Fojnica, A.; Osmanović, A.; Badnjević, A. (2016). Dynamical model of Tuberculosis-Multiple Strain Prediction based on artificial neural network. 5th Mediterranean Conference on Embedded Computing (MECO), pp. 290-293. https://doi.org/10.1109/MECO.2016.7525763
  3. Hwang, E.J.; Park, S.; Jin, K-N.; Kim, J.I.; Choi, S.Y.; Lee, J.H.; Goo, J.M.; Aum, J.; Yim, J-J.; Park, C.M. (2019). Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs. Clinical infectious diseases, 69(5), pp. 739-747. https://doi.org/10.1093/cid/ciy967
  4. Joshi, B.; Lestari, T.; Graham, S.M.; Baral, S.C.; Verma, S.C.; Ghimire, G.; Bhatta, B.; Dumre, S.P.; Utarini, A. (2018). The implementation of Xpert MTB/RIF assay for diagnosis of tuberculosis in Nepal: A mixed-methods analysis. PLoS One, 13(8). https://doi.org/10.1371/journal.pone.0201731
  5. Khan, M.T.; Kaushik, A.C.; Ji, L.; Malik, S.I.; Ali, S.; Wei, D-Q. (2019). Artificial Neural Networks for Prediction of Tuberculosis Disease. Frontiers in Microbiology, 10(395). https://doi.org/10.3389/fmicb.2019.00395
  6. Kitonsa, P.J.; Nalutaaya, A.; Mukiibi, J.; Nakasolya, O.; Isooba, D.; Kamoga, C.; Baik, Y.; Robsky, K.; Dowdy, D.W.; Katamba, A.; Kendall, E.A. (2020). Evaluation of underweight status may improve identification of the highest-risk patients during outpatient evaluation for pulmonary tuberculosis. PLoS One, 15(12). https://doi.org/10.1371/journal.pone.0243542
  7. Li, L.; Huang, H.; Jin, X. (2018). AE-CNN Classification of Pulmonary Tuberculosis Based on CT Images. 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 39-42. https://doi.org/10.1109/ITME.2018.00020
  8. Postnikov, E.; Esmedljaeva, D.; Lavrova, A. (2020). A CatBoost machine learning for prognosis of pathogen’s drug resistance in pulmonary tuberculosis. IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 86-87. https://doi.org/10.1109/LifeTech48969.2020.1570619054
  9. Rajaraman, S.; Candemir, S.; Xue, Z.; Alderson, O.; Kohli, M.; Abuya, J.; Antani, S. (2018). A novel stacked generalization of models for improved TB detection in chest radiographs. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 718-721. https://doi.org/10.1109/EMBC.2018.8512337
  10. Ramana, K.; Basha, S. (2004). Neural image recognition system with application to tuberculosis detection, International Conference on Information Technology: Coding and Computing, 2, pp. 694-698. https://doi.org/10.1109/ITCC.2004.1286735
  11. Rusdah; Winarko, E.; Wardoyo, R. (2015). Preliminary diagnosis of pulmonary tuberculosis using ensemble method. International Conference on Data and Software Engineering (ICoDSE), pp. 175-180. https://doi.org/10.1109/ICODSE.2015.7436993
  12. Shamshirband, S.; Hessam, S.; Javidnia, H.; Amiribesheli, M.; Vahdat, S.; Petković, D.; Gani, A.; Kiah, MLM. (2014). Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System. International. Journal of Medical Sciences, 11(5), pp. 508-514. https://doi.org/10.7150/ijms.8249
  13. Solh, El.; Hsiao, A.; Goodnough, C.; Serghani, S.; Grant, B. (1999). Predicting Active Pulmonary Tuberculosis Using an Artificial Neural Network. Chest Journal, 10(4), pp. 968–973. https://doi.org/10.1378/chest.116.4.968
  14. Souza, F.; Sanchez, M.; Seixas, J.M.; Maidantchik, C.; Galliez, R.; Moreira, ADSR.; da Costa, P.A.; Oliveira, M.M.; Harries, A.D.; Kritski, A.L. (2018). Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models. Tuberculosis, 111, pp. 94-101. https://doi.org/10.1016/j.tube.2018.05.012
  15. Temurtas, F.; Tanrikulu, A. (2010). Tuberculosis disease diagnosis using artificial neural networks. Journal of Medical Systems, 34(3), pp. 299-302. https://doi.org/10.1007/s10916-008-9241-x
  16. Vijayaraj, M.; Abhinand, P.A.; Venkatesan, P.; Ragunath, P.K. (2020). An ANN model for the differential diagnosis of tuberculosis and sarcoidosis. Bioinformation, 16(7), pp. 539-546. https://doi.org/10.6026/97320630016539
  17. World Health Organization. (2021). Global tuberculosis report 2021, Global Tuberculosis Programme, 25 p. https://www.who.int/publications/i/item/9789240037021
  18. Wu, Y.; Wang, H.; Wu, F. (2017). Automatic classification of pulmonary tuberculosis and sarcoidosis based on random forest. 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-5. https://doi.org/10.1109/CISP-BMEI.2017.8302280
  19. Xing, Z.; Ding, W.; Zhang, S.; Zhong, L.; Wang, L.; Wang, J.; Wang, K.; Xie, Y.; Zhao, X.; Li, N.; Ye, Z. (2020). Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images. Biomed Research International. https://doi.org/10.1155/2020/6287545
  20. Yang, A.; Jin, X.; Li, L. (2019). CT Images Recognition of Pulmonary Tuberculosis Based on Improved Faster RCNN and U-Net. 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 93-97. https://doi.org/10.1109/ITME.2019.00032

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