Assessing Diagnostic Accuracy and Viability of AI-Assisted Tuberculosis Detection in Northern Indian Healthcare Facilities: A Multicenter Study

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Abstract
Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of high-risk individuals. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed “DecXpert” a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4,363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI: 0.85-0.93) and 85% specificity (95% CI: 0.82-0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI: 0.88-0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable identifying high-risk individuals and facilitate effective TB management where skilled radiological interpretation is limited.

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