ArticleMicroorganisms2024
Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears.
Article in Microorganisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- A smear test aiding with an automated slide reader acquires equivalent sensitivity to MGIT960 culture in pulmonary tuberculosis diagnosis.Microbiology spectrum · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Development of a clinical algorithm-based scoring system to diagnose smear-negative pulmonary tuberculosis in Sabah, Malaysia using the modified Delphi method.Journal of global health · 2026Article
- Distinct mechanisms drive post-antibiotic Tuberculosis relapse post-cure versus post-treatment-failure.bioRxiv : the preprint server for biology · 2026Article
- A Systematic Review and Meta-analysis on Innovative Approaches in Tuberculosis Diagnosis: Challenges and Future Directions.International journal of MCH and AIDS · 2026Review
- Review
- Review
- Peripheral blood neutrophil proteomic profiling with transcriptomic data integration reveals biomarkers for tuberculosis infection diagnosis.EBioMedicine · 2025Article
- Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.Microbiology spectrum · 2025Article
- Fast Screening of Tuberculosis Patients Based on Analysis of Plasma by Infrared Spectroscopy Coupled with Machine Learning Approaches.ACS omega · 2025Article
- Tuberculosis diagnosis using artificial intelligence: current trends and future prospects.Frontiers in medicine · 2025Review
- A case of thoracic aortic aneurysm with long-term hoarseness as the sole symptom.Tuberkuloz ve toraks · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopic examination of acid-fast mycobacterial bacilli (AFB) in sputum smears remains the most economical and readily available method for laboratory diagnosis of pulmonary tuberculosis (TB). However, this conventional approach is low in sensitivity and labor-intensive. An automated microscopy system incorporating artificial intelligence and machine learning for AFB identification was evaluated. The study was conducted at an infectious disease hospital in Jiangsu Province, China, utilizing an intelligent microscope system. A total of 1000 sputum smears were included in the study, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFBs. Referee technicians served as the gold standard for discrepant results. The automated system demonstrated an overall accuracy of 96.70% (967/1000), sensitivity of 91.94% (194/211), specificity of 97.97% (773/789), and negative predictive value (NPV) of 97.85% (773/790) at a prevalence of 21.1% (211/1000). Incorporating AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.