Development and Validation of Deep Learning-Based Infectivity Prediction 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 traditional diagnostic 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 of patients 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 local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing sp

SUBMITTER: Chung WY 

PROVIDER: S-EPMC11582483 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature