一个创新的模型用于诊断冠状动脉血管图像中的病变,使用改进的YOLOv4模型
Zhu Chen1,2,3, Yajie Chen1, Jiajia Si3
1Institute for Future Sciences & Hengyang Medical School, University of South China, Hengyang 421001, China.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
一个改进的YOLOv4 AI模型准确地检测急性心肌梗塞 (AMI) 患者的冠状动脉病变. 这种人工智能工具显示了预测主要不良心血管事件 (MACE) 的潜力.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 通过血管造影诊断冠状动脉疾病是复杂和主观的.
- 需要对冠状动脉血管学 (CAG) 数据进行自动化分析,以提高效率和准确性.
- 急性心肌梗塞 (AMI) 患者数据需要对预后评估进行可靠的解释.
研究的目的:
- 开发和验证一个改进的YOLOv4算法,用于冠状动脉病变的自动检测和分类.
- 为了将AI检测到的病变特征与主要不良心血管事件 (MACE) 相关联.
- 为AMI患者建立一个人工智能辅助的诊断和预后工具.
主要方法:
- 从408名AMI患者的CAG数据的回顾性收集和预处理.
- 开发了一种改进的YOLOv4算法,在VOC数据集上进行验证,并在CAG数据上进行微调.
- 在随访期间,模型衍生病变特征和MACEs发生之间的统计相关性分析.
主要成果:
- 改进的YOLOv4模型在VOC数据集上实现了84.72%的mAP,在冠状动脉损伤检测中达到55.01%的mAP.
- 完全封闭的病变 (Log-rank p = 0.003) 和多分支病变 (Log-rank p = 0.033) 与MACE有显著的相关性.
- 该模型在检测和分类冠状动脉病变方面表现强.
结论:
- 改进的YOLOv4模型提供了一个有效和准确的AI辅助工具,用于AMI的冠状动脉损伤分析.
- 人工智能衍生的解剖特征可以通过预测MACEs,为长期预后评估提供初步见解.
- 这项研究将深度学习与临床结果相结合,为人工智能在心血管诊断和预后方面铺平了道路.
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