Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.

<h4>Background and objectives</h4>Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluat...

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Autores principales:	Bumi Herman, Wandee Sirichokchatchawan, Sathirakorn Pongpanich, Chanin Nantasenamat
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d6830cbb3b464faabc6a45819fd45c0f
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spelling	oai:doaj.org-article:d6830cbb3b464faabc6a45819fd45c0f2021-11-25T06:19:23ZDevelopment and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.1932-620310.1371/journal.pone.0249243https://doaj.org/article/d6830cbb3b464faabc6a45819fd45c0f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0249243https://doaj.org/toc/1932-6203<h4>Background and objectives</h4>Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool.<h4>Methods</h4>A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment.<h4>Results</h4>A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85-91)), and sensitivity (84% (95% CI 76-89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86-93)) where Logistic Regression demonstrates the highest (99% (95% CI 97-99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%).<h4>Conclusion</h4>The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available.<h4>Trial registration</h4>NCT04208789.Bumi HermanWandee SirichokchatchawanSathirakorn PongpanichChanin NantasenamatPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 3, p e0249243 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bumi Herman Wandee Sirichokchatchawan Sathirakorn Pongpanich Chanin Nantasenamat Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
description	<h4>Background and objectives</h4>Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool.<h4>Methods</h4>A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment.<h4>Results</h4>A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85-91)), and sensitivity (84% (95% CI 76-89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86-93)) where Logistic Regression demonstrates the highest (99% (95% CI 97-99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%).<h4>Conclusion</h4>The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available.<h4>Trial registration</h4>NCT04208789.
format	article
author	Bumi Herman Wandee Sirichokchatchawan Sathirakorn Pongpanich Chanin Nantasenamat
author_facet	Bumi Herman Wandee Sirichokchatchawan Sathirakorn Pongpanich Chanin Nantasenamat
author_sort	Bumi Herman
title	Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
title_short	Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
title_full	Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
title_fullStr	Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
title_full_unstemmed	Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.
title_sort	development and performance of cuhas-robust application for pulmonary rifampicin-resistance tuberculosis screening in indonesia.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d6830cbb3b464faabc6a45819fd45c0f
work_keys_str_mv	AT bumiherman developmentandperformanceofcuhasrobustapplicationforpulmonaryrifampicinresistancetuberculosisscreeninginindonesia AT wandeesirichokchatchawan developmentandperformanceofcuhasrobustapplicationforpulmonaryrifampicinresistancetuberculosisscreeninginindonesia AT sathirakornpongpanich developmentandperformanceofcuhasrobustapplicationforpulmonaryrifampicinresistancetuberculosisscreeninginindonesia AT chaninnantasenamat developmentandperformanceofcuhasrobustapplicationforpulmonaryrifampicinresistancetuberculosisscreeninginindonesia
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Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.

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