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Published on: August 30, 2013
AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study
Mohammad T Elhakim1, Sarah W Stougaard1, Ole Graumann1
1From the Department of Radiology (M.T.E., L.B.L., B.S.B.R.), Department of Nuclear Medicine (O. Gerke), and CAI-X-Centre for Clinical Artificial Intelligence (B.S.B.R.), Odense University Hospital, Kløvervænget 10, Entrance 112, 2nd Floor, 5000 Odense C, Denmark; Department of Clinical Research, Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark (M.T.E., S.W.S., O. Graumann, O. Gerke, B.S.B.R.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (O. Graumann); Department of Clinical Research, Aarhus University, Aarhus, Denmark (O. Graumann); and Department of Computer Science, University of Copenhagen, Copenhagen, Denmark (M.N.).
Abstract:
Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AIfirst), the second reader (scenario 2: integrated AIsecond), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AItriage). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AIfirst showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, P < .001). Integrated AIsecond had lower sensitivity (-1.58%, P < .001), negative predictive value (NPV) (-0.01%, P < .001), and recall rate (-0.06%, P = .04) but a higher positive predictive value (PPV) (+0.03%, P < .001) and arbitration rate (+1.22%, P < .001). Integrated AItriage achieved higher sensitivity (+1.33%, P < .001), PPV (+0.36%, P = .03), and NPV (+0.01%, P < .001) but lower arbitration rate (-0.88%, P < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. Keywords: Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license.
