Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization.

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第一作者： Martina,Cotena

第一单位： Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.

作者： Martina,Cotena ^[1] ; Angela,Ayobi ^[1] ; Colin,Zuchowski ^[2] ; Jacqueline C,Junn ^[2] ; Brent D,Weinberg ^[2] ; Peter D,Chang ^[3] ; Daniel S,Chow ^[3] ; Jennifer E,Soun ^[3] ; Mar,Roca-Sogorb ^[1] ; Yasmina,Chaibi ^[1] ; Sarah,Quenet ^[1]

作者单位： Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France. ^[1] Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USA. ^[2] Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA.;Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA. ^[3]

关键词 aortic dissection automated detection deep learning emergency radiology multi-reader multi-case study prioritized worklist

DOI 10.3390/diagnostics14232689

PMID 39682597

发布时间 2025-01-04

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Diagnostics (Basel, Switzerland)

2024年14卷23期

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Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization.

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Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization.

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