ArticleSensors (Basel, Switzerland)2022
Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Recent advances in antituberculosis drugs and therapeutics: from mechanistic breakthroughs to clinical translation.Molecular biomedicine · 2026Review
- [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
- A Systematic Review and Meta-analysis on Innovative Approaches in Tuberculosis Diagnosis: Challenges and Future Directions.International journal of MCH and AIDS · 2026Review
- Advancements in laboratory diagnostics for HIV/MTB coinfection: Integrating conventional methods with emerging technologies.Practical laboratory medicine · 2025Review
- Automatic identification of clinically importantEmerging microbes & infections · 2025Article
- Lab-in-a-cartridge for real-time detection of tuberculosis via precise measurement of urinary lipoarabinomannan.Nature communications · 2025Article
- Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.Microbiology spectrum · 2025Article
- Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.Frontiers in cellular and infection microbiology · 2025Review
- AI-Based Analysis of Ziehl-Neelsen-Stained Sputum Smears forLife (Basel, Switzerland) · 2024Article
- Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches.Diseases (Basel, Switzerland) · 2024Review
- A case of thoracic aortic aneurysm with long-term hoarseness as the sole symptom.Tuberkuloz ve toraks · 2024Article
- Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears.Microorganisms · 2024Article
- Retrospective validation of MetaSystems' deep-learning-based digital microscopy platform with assistance compared to manual fluorescence microscopy for detection of mycobacteria.Journal of clinical microbiology · 2024Article
- Comparative analysis of five etiological detecting techniques for the positive rates in the diagnosis of tuberculous granuloma.Journal of clinical tuberculosis and other mycobacterial diseases · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
The most robust and economical method for laboratory diagnosis of tuberculosis (TB) is to identify mycobacteria acid-fast bacilli (AFB) under acid-fast staining, despite its disadvantages of low sensitivity and labor intensity. In recent years, artificial intelligence (AI) has been used in TB-smear microscopy to assist medical technologists with routine AFB smear microscopy. In this study, we evaluated the performance of a TB automated system consisting of a microscopic scanner and recognition program powered by artificial intelligence and machine learning. This AI-based system can detect AFB and classify the level from 0 to 4+. A total of 5930 smears were evaluated on the performance of this automatic system in identifying AFB in daily lab practice. At the first stage, 120 images were analyzed per smear, and the accuracy, sensitivity, and specificity were 91.3%, 60.0%, and 95.7%, respectively. In the second stage, 200 images were analyzed per smear, and the accuracy, sensitivity, and specificity were increased to 93.7%, 77.4%, and 96.6%. After removing disqualifying smears caused by poor staining quality and smear preparation, the accuracy, sensitivity, and specificity were improved to 95.2%, 85.7%, and 96.9%, respectively. Furthermore, the automated system recovered 85 positive smears initially identified as negative by manual screening. Our results suggested that the automated TB system could achieve higher sensitivity and laboratory efficiency than manual microscopy under the quality control of smear preparation. Automated TB smear screening systems can serve as a screening tool at the first screen before manual microcopy.
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