基于深度学习的表面大腿动脉病变的分类:试点研究
Yo Sep Lee1, Choongmin Kim2, Youngje Woo1
1Division of Vascular and Transplant Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Annals of surgical treatment and research
|February 13, 2026
概括
对象检测算法显示出在CT血管图上识别外围动脉疾病病变的前景. 虽然对于支架有效,但需要进一步改进以准确检测狭窄和闭塞.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 血管疾病 血管疾病
背景情况:
- 周围动脉疾病 (PAD) 显著影响患者的发病率和死亡率.
- 准确检测PAD病变,包括狭窄和闭塞,对于有效治疗至关重要.
- 计算机断层扫描 (CT) 血管造影是评估下肢血管系统的关键成像方式.
研究的目的:
- 评估使用物体检测算法在下肢CT血管图中识别外围动脉病变的可行性.
- 为了比较一阶段 (YOLOv5) 和两阶段 (Faster R-CNN) 对象检测模型在分类血管病变中的性能.
主要方法:
- 从PAD患者的1,241个对比度增强的下肢CT血管图的回顾性分析.
- 应用YOLOv5 (v5s,v5m,v5l,v5x) 和Faster R-CNN模型来检测和分类支架,狭窄和闭塞.
- 使用平均平均精度 (mAP@.5) 和精度回忆曲线进行性能评估,将算法结果与手动注释进行比较.
主要成果:
- YOLOv5l获得了最高的整体精度 (77% mAP@.5).
- 支架分类的准确性很高 (≥96.9% mAP@.5),而支架狭窄 (53.8%-58.7%) 和闭塞 (69%-80.9%) 的检测显示中等准确性.
- 观察到狭窄和错过分叉处的闭塞的高假阴性率,其中一些YOLOv5模型表现出过拟合.
结论:
- 对象检测算法证明了其可行性,作为CT血管学中PAD病变检测的初步工具.
- 需要通过增强训练数据和病变注释进行进一步的研究,以提高狭窄和闭塞的分类准确性.
- 这些算法的开发可能会导致未来用于PAD管理的临床决策支持系统.
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