Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

ABSTRACT: Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant t

SUBMITTER: Liang S 

PROVIDER: S-EPMC10925488 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature