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Machine learning has been an emerging tool for various aspects of infectiousdiseases including tuberculosis surveillance and detection. However, WHOprovided no recommendations on using computer-aided tuberculosis detectionsoftware because of the small number of studies, methodological limitations,and limited generalizability of the findings. To quantify the generalizabilityof the machine-learning model, we developed a Deep Convolutional Neural Network(DCNN) model using a TB-specific CXR dataset of one population (NationalLibrary of Medicine Shenzhen No.3 Hospital) and tested it with non-TB-specificCXR dataset of another population (National Institute of Health ClinicalCenters). The findings suggested that a supervised deep learning modeldeveloped by using the training dataset from one population may not have thesame diagnostic performance in another population. Technical specification ofCXR images, disease severity distribution, overfitting, and overdiagnosisshould be examined before implementation in other settings.

Deep Learning for Automated Classification of Tuberculosis-Related Chest X-Ray: Dataset Specificity Limits Diagnostic Performance Generalizability