Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs.

ABSTRACT:

Background

Chest X-ray (CXR) interpretation is critical in diagnosing various lung diseases. However, physicians, not specialists, are often the first ones to read them, frequently facing challenges in accurate interpretation. Artificial Intelligence (AI) algorithms could be of great help, but using real-world data is crucial to ensure their effectiveness in diverse healthcare settings. This study evaluates a deep learning algorithm designed for CXR interpretation, focusing on its utility for non-specialists in thoracic radiology physicians.

Purpose

To assess the performance of a Convolutional Neural Networks (CNNs)-based AI algorithm in interpreting CXRs and compare it with a team of physicians, including thoracic radiologists, who served as the gold-standard.

Methods

SUBMITTER: de Camargo TFO 

PROVIDER: S-EPMC11843218 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature