Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities.

ABSTRACT:

Background

Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.

Methods

The performance of two cloud-based CXR AI systems - one to detect TB and the other to detect CXR abnormalities - in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites. The TB-detecting AI (TB AI) scores were converted to binary using two thresholds: a high-sensitivity threshold and an exploratory threshold designed to resemble radiologist performance. Ten radiologists reviewed images for signs of TB,

SUBMITTER: Kazemzadeh S 

PROVIDER: S-EPMC11737584 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature