Abstract：
OBJECTIVE: This 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.
METHODS: A 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.
RESULTS: RealCAC-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.
CONCLUSIONS: RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.
METHODS: A 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.
RESULTS: RealCAC-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.
CONCLUSIONS: RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.
摘要：
目的：本研究介绍了RealCAC-Net，人工智能（AI）系统，量化颈动脉可压缩性（CAC）并确定心肺复苏期间自发循环的恢复（ROSC）。
方法：一项基于韩国急诊科2022年至2023年数据的前瞻性研究，使用新型AI模型研究了成年心脏骤停患者的颈动脉可压缩性。RealCAC-Net。数据包括161例病例的11,958张训练图像和134例病例的15,080张测试图像。RealCAC-Net分三个步骤处理图像：基于TransUNet的分割，颈动脉可压缩性测量算法，用于改进分割和CAC计算，和基于CAC的分类从0（表示圆形）到1（表示高压缩）。使用骰子相似系数、相交-联合，精度,召回，F1得分。
结果：RealCAC-Net，应用颈动脉可压缩性测量算法，在交叉验证中表现优于基线模型，骰子的平均相似系数为0.90，交叉对并为0.84，分类精度为0.96。测试集实现了0.96的分类准确性和0.97的F1评分，证明了其在心脏骤停情况下准确识别ROSC的功效。
结论：RealCAC-Net能够精确定量心肺复苏期间的ROSC测定。未来的研究应该整合这种AI增强的超声方法来彻底改变急诊护理。
方法：一项基于韩国急诊科2022年至2023年数据的前瞻性研究，使用新型AI模型研究了成年心脏骤停患者的颈动脉可压缩性。RealCAC-Net。数据包括161例病例的11,958张训练图像和134例病例的15,080张测试图像。RealCAC-Net分三个步骤处理图像：基于TransUNet的分割，颈动脉可压缩性测量算法，用于改进分割和CAC计算，和基于CAC的分类从0（表示圆形）到1（表示高压缩）。使用骰子相似系数、相交-联合，精度,召回，F1得分。
结果：RealCAC-Net，应用颈动脉可压缩性测量算法，在交叉验证中表现优于基线模型，骰子的平均相似系数为0.90，交叉对并为0.84，分类精度为0.96。测试集实现了0.96的分类准确性和0.97的F1评分，证明了其在心脏骤停情况下准确识别ROSC的功效。
结论：RealCAC-Net能够精确定量心肺复苏期间的ROSC测定。未来的研究应该整合这种AI增强的超声方法来彻底改变急诊护理。
