ArticleDiagnostics (Basel, Switzerland)2022
Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.BMC medical imaging · 2024Pooled it
- Advanced diagnostic methods for nontuberculous mycobacterial infections.Frontiers in tuberculosis · 2026Review
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis.Frontiers in microbiology · 2025Review
- Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges.Frontiers in microbiology · 2025Review
- MicroMagnify: A Multiplexed Expansion Microscopy Method for Pathogens and Infected Tissues.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A Study ofDiagnostics (Basel, Switzerland) · 2023Article
- MicroMagnify: a multiplexed expansion microscopy method for pathogens and infected tissues.Research square · 2023Article
- Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification.Translational lung cancer research · 2022Article
- A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl-Neelsen Stain on Tissue.Diagnostics (Basel, Switzerland) · 2022Article
- AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The histopathological diagnosis of mycobacterial infection may be improved by a comprehensive analysis using artificial intelligence. Two autopsy cases of pulmonary tuberculosis, and forty biopsy cases of undetected acid-fast bacilli (AFB) were used to train AI (convolutional neural network), and construct an AI to support AFB detection. Forty-two patients underwent bronchoscopy, and were evaluated using AI-supported pathology to detect AFB. The AI-supported pathology diagnosis was compared with bacteriology diagnosis from bronchial lavage fluid and the final definitive diagnosis of mycobacteriosis. Among the 16 patients with mycobacteriosis, bacteriology was positive in 9 patients (56%). Two patients (13%) were positive for AFB without AI assistance, whereas AI-supported pathology identified eleven positive patients (69%). When limited to tuberculosis, AI-supported pathology had significantly higher sensitivity compared with bacteriology (86% vs. 29%,
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