ArticleDiagnostics (Basel, Switzerland)2022
A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl-Neelsen Stain on Tissue.
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 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 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
- Recent advances in antituberculosis drugs and therapeutics: from mechanistic breakthroughs to clinical translation.Molecular biomedicine · 2026Review
- Validation of an AI-Driven Sputum Analysis Platform for Accurate and Reliable Tuberculosis Detection in South-Western Uganda.Medical devices (Auckland, N.Z.) · 2026Article
- Efficient Tissue Detection in Whole-Slide Images Using Classical and Hybrid Methods: Benchmark on TCGA Cancer Cohorts.Cancers · 2025Article
- Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.Microbiology spectrum · 2025Article
- Advancing the fight against tuberculosis: integrating innovation and public health in diagnosis, treatment, vaccine development, and implementation science.Frontiers in medicine · 2025Review
- AI-Based Analysis of Ziehl-Neelsen-Stained Sputum Smears forLife (Basel, Switzerland) · 2024Article
- Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.PLoS neglected tropical diseases · 2024Article
- First detection of bovine tuberculosis by Ziehl-Neelsen staining and polymerase chain reaction at dairy farms in the Lekok Sub-District, Pasuruan Regency, and Surabaya region, Indonesia.Veterinary world · 2024Article
- Evolution of Laboratory Diagnosis of Tuberculosis.Clinics and practice · 2024Review
- Article
- Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls.Diagnostics (Basel, Switzerland) · 2023Article
- Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis".Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl-Neelsen (ZN)-stained slides is a long task requiring significant pathologist's effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of over 260,000 positive and over 700,000,000 negative patches annotated on scans of 510 whole slide images (WSI) of ZN-stained slides (110 positive and 400 negative). Several image augmentation techniques coupled with different custom computer vision architectures were used. WSIs automatic analysis was followed by a report indicating areas more likely to present mycobacteria. Our model performs AI-based diagnosis (the final decision of the diagnosis of WSI belongs to the pathologist). The results were validated internally on a dataset of 286,000 patches and tested in pathology laboratory settings on 60 ZN slides (23 positive and 37 negative). We compared the pathologists' results obtained by separately evaluating slides and WSIs with the results given by a pathologist aided by automatic analysis of WSIs. Our architecture showed 0.977 area under the receiver operating characteristic curve. The clinical test presented 98.33% accuracy, 95.65% sensitivity, and 100% specificity for the AI-assisted method, outperforming any other AI-based proposed methods for AFB detection.
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