ArticleMayo Clinic proceedings. Digital health2026
Computer-Aided Analysis of Photographed Chest X-Ray Films Performs Well Compared With Trained Radiologists.
Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To assess whether computer-aided detection (CAD) chest X-ray (CXR) software may aid physicians in low-resource, high tuberculosis (TB) endemic settings where radiologists are scarce. Patients and Methods: A retrospective pilot study was conducted on CXR films taken between January 1, 2017, and March 30, 2018, in Guinea-Bissau and Ethiopia to compare the interpretation of CXRs regarding pulmonary TB (PTB) by CAD (qXR; Qure.ai) with that of 2 experienced Ethiopian radiologists (A and B). To improve the applicability of this method in low-resource settings, an analysis was performed on images of CXRs taken by mobile phones. Two reference standards were applied: final PTB diagnosis by clinical or laboratory findings (ie, Xpert MTB/RIF [Xpert]-confirmed PTB). Results: We included 498 CXRs from patients seeking help for TB indicative symptoms. Radiologist A identified 50, radiologist B identified 99, and the software identified 81 as indicative of TB. The overall area under the curve for the receiver-operating characteristic curve of the software was 0.84 for Xpert-confirmed cases. At the prechosen cutoff value of 0.5, the sensitivity of CAD CXR was 76.5%, and the specificity was 85.9%. Radiologist A's assessments were 64.7% sensitive and 91.9% specific, whereas radiologist B's assessments were 76.5% sensitive and 82.3% specific for Xpert-confirmed cases. The agreement regarding TB-related findings between the radiologists combined (κ=0.45) and each radiologist and the software (κ=0.56) was moderate. Conclusion: Our study revealed that CAD CXR performs comparably with experienced radiologists when it is applied to CXR films, photographed by mobile phones and a digital camera with similar sensor resolutions. Trial registration: PACTR201611001838365.
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