Publication:
Development and performance of CUHASROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

dc.contributor.authorBumi Hermanen_US
dc.contributor.authorWandee Sirichokchatchawanen_US
dc.contributor.authorSathirakorn Pongpanichen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.otherChulalongkorn Universityen_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2022-08-04T11:41:23Z
dc.date.available2022-08-04T11:41:23Z
dc.date.issued2021-03-01en_US
dc.description.abstractBackground and objectives 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 CUHASROBUST model performance, an artificial-intelligence-based RR-TB screening tool. Methods 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. Results 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%). Conclusion 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.en_US
dc.identifier.citationPLoS ONE. Vol.16, No.3 March (2021)en_US
dc.identifier.doi10.1371/journal.pone.0249243en_US
dc.identifier.issn19326203en_US
dc.identifier.other2-s2.0-85103276881en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/79374
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103276881&origin=inwarden_US
dc.subjectMultidisciplinaryen_US
dc.titleDevelopment and performance of CUHASROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103276881&origin=inwarden_US

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