A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for TB

This is a new systematic review on the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities ( computer-aided detection , or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). The review included 53 studies: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). The authors conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. They provide concrete suggestions on what study design elements should be improved.

URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221339

Harris PLOS ONE 2019.pdf (1.7 MB)