aceDiagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal
Objectives To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest radiographs of health assessment applicants at a tertiary hospital in Nepal. Design Prospective, single-centre, shadow-mode diagnostic accuracy validation study. Reported in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 checklist and STARD-Artificial Intelligence (AI)/Developmental and Exploratory Clinical Investigations of…
Despite preserved discrimination, the algorithm showed calibration failure with compressed scores, low positive predictive value and low agreement, indicating distributional shift in this low- and middle-income country setting.
Single-reader reference standard, small number of positives creating wide uncertainty, and lack of independent external validation limit generalizability before operational deployment.
Evidence
- Peer-reviewedBMJ Open2026-10-01
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Truvace Impact Record TRV-2026-1265, v1: “Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal.” Truvace, 2026-10-03. /record/TRV-2026-1265 (accessed at citation time). sha256 749b5f04d9603132…
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