Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

Angela Ayobi¹, Adam Davis², Peter D Chang^3,4, Daniel S Chow^3,4, Kambiz Nael⁵, Maxime Tassy⁶, Sarah Quenet⁶, Sylvain Fogola⁶, Peter Shabe⁷, David Fussell³, Christophe Avare⁶, Yasmina Chaibi⁶

¹From Avicenna.AI (A.A., M.T., S.Q., S.F., C.A., Y.C.), La Ciotat, France angela.ayobi@avicenna.ai.

²Amalgamated Vision (A.D.), Brentwood, Tennessee.

³Department of Radiological Sciences (P.D.C., D.S.C., D.F.), University of California Irvine, Orange, California.

⁴Center for Artificial Intelligence in Diagnostic Medicine (P.D.C., D.S.C.), University of California Irvine, Irvine, California.

⁵David Geffen School of Medicine at UCLA (K.N.), Los Angeles, California.

⁶From Avicenna.AI (A.A., M.T., S.Q., S.F., C.A., Y.C.), La Ciotat, France.

⁷Advance Research Associates (P.S.), Santa Clara, California.

PubMed PMC DOI PDF

Abstract

BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well-documented selection criterion for acute ischemic stroke treatment; however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with notable interobserver variabilities. We conducted a multireader, multicase study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-base…

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