Multicenter, Multinational, and Multivendor Validation of an Artificial Intelligence Application for Acute Cervical Spine Fracture Detection on CT.

Jinkyeong Sung^1,2, Peter D Chang^1,3, Angela Ayobi⁴, Martina Cotena⁴, Mar Roca-Sogorb⁴, Jinhee Jang^1,5, Daniel S Chow³, Yasmina Chaibi⁴

¹Applied Artificial Intelligence Research, University of California Irvine, 836 Health Sciences Road, Suite 4021, Irvine, CA 92617, USA.

²Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06591, Republic of Korea.

³Department of Radiological Sciences, University of California Irvine, 1 Medical Plaza Dr, Irvine, CA 92697, USA.

⁴Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.

⁵Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06973, Republic of Korea.

PubMed PMC DOI PDF

Abstract

Background/Objectives: While previous studies have evaluated AI algorithms for cervical spine fracture (CSFx) detection on CT, many have lacked validation on diverse, multinational datasets or have focused primarily on overall case-level classification This study aimed to evaluate the performance of an AI application for acute CSFx detection in case-level classification, fracture localization, and…

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