Evaluation of an artificial intelligence (AI) system to detect tuberculosis on chest X-ray at a pilot active...

Although computer-aided detection (CAD) software employed with Artificial Intelligence (AI) system has been developed aiming to assist tuberculosis (TB) triage, screening, and diagnosis, its clinical performance for tuberculosis screening remains unknown.

To evaluate performance of an CAD software for detecting TB on chest X-ray images at a pilot active TB screening project.

A CAD software scheme employed with AI was used to screen chest X-ray images of participants and produce probability scores of cases being positive for TB. CAD-generated TB detection scores were compared with on-site and senior radiologists via several performance evaluation indices including area under the ROC curves (AUC), specificity, sensitive, and positive predict value. Pycharm CE and SPSS statistics software packages were used for data analysis.

Among 2,543 participants, eight TB patients were identified from this screening pilot program. The AI-based CAD system outperformed the onsite (AUC = 0.740) and senior radiologists (AUC = 0.805) either using thresholds of 30% (AUC = 0.978) and 50% (AUC = 0.859) when taking the final diagnosis as the ground truth.

The AI-based CAD software successfully detects all TB patients as identified from this study at a threshold of 30%. It demonstrates feasibility and easy accessibility to carry out large scale TB screening using this CAD software equipped in medical vans with chest X-ray imaging machine.