• Subin Park, Hee Yoon, Yeon KangSooSDepartment of Emergency Medicine, Chung-ang University Gwangmyeong Hospital, Gwangmyeong-si, Gyeonggi-do, Republic of Korea, 14353., Joon JoIkIDepartment of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea, 06351., Sejin Heo, Hansol Chang, Eun ParkJongJDepartment of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea, 06351., Guntak Lee, Taerim Kim, Yeon HwangSungSDepartment of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea, 06351., Soyoung Park, and Jin ChungMyungMDepartment of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 0631, Republic of Korea; Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Republic of.
• Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea.
• Resuscitation. 2024 Jul 5: 110302110302.

AimThis study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation.MethodsA prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score.ResultsRealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations.ConclusionsRealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

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