Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests.

ABSTRACT:

Background

Tuberculosis (TB) is a chronic respiratory infectious disease caused by Mycobacterium tuberculosis, typically diagnosed through sputum smear microscopy for acid-fast bacilli (AFB) to assess the infectivity of TB.

Methods

This study enrolled 769 patients, including 641 patients from the First Affiliated Hospital of Guangxi Medical University as the training group, and 128 patients from Guangxi Hospital of the First Affiliated Hospital of Sun Yat-sen University as the validation group. Among the training cohort, 107 patients were AFB-positive, and 534 were AFB-negative. In the validation cohort, 24 were AFB-positive, and 104 were AFB-negative. Blood samples were collected and analyzed using machine learning (ML) methods to identify key factors for TB diagnosis.

R

SUBMITTER: Zhu J 

PROVIDER: S-EPMC11803159 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature