ArticleFrontiers in public health2022
Machine learning in the loop for tuberculosis diagnosis support.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.Nursing & health sciences · 2025Pooled it
- Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models.BMC infectious diseases · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- A Framework for Two-class Classification of Pulmonary Tuberculosis using Artificial IntelligenceCurrent medical imaging · 2025Article
- Development of a clinical prediction model for poor treatment outcomes in the intensive phase in patients with initial treatment of pulmonary tuberculosis.Frontiers in medicine · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Early detection of tuberculosis: a systematic review.Pneumonia (Nathan Qld.) · 2024Review
- Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.JMIR research protocols · 2024Article
- Model for predicting drug resistance based on the clinical profile of tuberculosis patients using machine learning techniques.PeerJ. Computer science · 2024Article
- Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.Annals of medicine · 2023Article
- A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network.Frontiers in genetics · 2023Article
- TubIAgnosis: A machine learning-based web application for active tuberculosis diagnosis using complete blood count data.Digital healthArticle
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Authors and funding
5 authors.
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Abstract
The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.
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