College of Public Health Science, Chulalongkorn University, Bangkok, Thailand.
Faculty of Medical Technology, Mahidol University, Salaya, Nakhon Pathom, Thailand.
PLoS One. 2021 Mar 25;16(3):e0249243. doi: 10.1371/journal.pone.0249243. eCollection 2021.
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.
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.
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%).
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.
NCT04208789.
用药物敏感性试验(DST)诊断肺利福平耐药结核病(RR-TB)既昂贵又耗时。此外，在印度尼西亚，快速诊断的GeneXpert 并不广泛应用。本研究旨在开发和评估基于人工智能的 RR-TB 筛查工具——CUHAS-ROBUST 模型的性能。
这是一项横断面研究，纳入了来自印度尼西亚哈桑努丁大学医学院附属医院的疑似所有类型 RR-TB 患者，这些患者均完成了完整的痰罗氏低氏培养(DST)(参考)和 19 项临床、实验室和影像学参数的检测，这些参数均从患者的病历中获取。沿着人工神经网络(ANN)模型和其他分类器构建模型。对 2020 年 1 月至 10 月招募的参与者进行了模型测试，并将其部署到 CUHAS-ROBUST(指标测试)应用程序中。通过评估获得了敏感性、特异性和准确性。
共纳入 487 名参与者(32 名耐多药/多耐药 MDR 57 RR-TB，398 名药物敏感)用于模型构建，157 名参与者(23 名 MDR 和 21 名 RR)用于前瞻性测试。与其他模型相比，全 ANN 模型具有最高的准确性值(88%(95%CI 85-91%)和敏感性(84%(95%CI 76-89%))，而其他模型的敏感性均低于 80%(Logistic Regression 32%、决策树 44%、随机森林 25%、极端梯度提升 25%)。然而，该 ANN 在其他模型中特异性较低(90%(95%CI 86-93%))，而 Logistic Regression 的特异性最高(99%(95%CI 97-99%))。该 ANN 模型被选入 CUHAS-ROBUST 应用程序，尽管它的敏感性仍低于全球 GeneXpert 结果(87.5%)。
ANN-CUHAS ROBUST 在检测所有类型的 RR-TB 方面优于其他人工智能分类器模型，通过将其应用于该程序，医务人员可以将该工具用于筛查目的，特别是在基层医疗保健水平GeneXpert 检查不可用的地方。
NCT04208789。
