Development and Validation of Deep Learning-Based InfectivityPrediction in Pulmonary Tuberculosis Through Chest Radiography:Retrospective Study

Abstract

Background: Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditionaldiagnostic tests such as smear and culture tests, which can require hours to weeks to yield results.Objective: This study aimed to use artificial intelligence (AI)-based chest radiography (CXR) to evaluate the infectivity ofpatients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests.Methods: We used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and localinterpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXRimages of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentationand cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from YonginSeverance Hospital for external validation.Results: In the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristiccurve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%,an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition,gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into thedecision-making process of the AI model.Conclusions: This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, withsignificant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings,compared with traditional smear and culture tests.