完全自动的下肢动脉堵塞的严重程度分级,基于足部 perfusion 成像
概括
一种新的基于摄像头的方法使用脚部输液有效地分级下肢动脉阻塞 (LLAB) 的严重程度. 该ResNet18-CL模型显著提高了早期检测周围动脉疾病 (PAD) 的分级准确性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 医疗保健中的机器学习
背景情况:
- 下肢动脉堵塞 (LLAB) 是外周动脉疾病 (PAD) 的关键指标.
- 目前用于LLAB的诊断方法可能是侵入性的或昂贵的.
- 脚部输液成像提供了一个非侵入性的方法来评估LLAB.
研究的目的:
- 评估机器学习方法,以使用足部 perfusion 图像对 LLAB 严重程度进行分级.
- 开发和验证一个新的深度学习模型ResNet18-CL,以改进LLAB严重性分类.
主要方法:
- 模拟LLAB通过对20名健康参与者的小牛施加五个压力级.
- 收集的足部视频数据和生成的远程光电显微镜 (rPPG) 图像.
- 他对四种传统的机器学习算法进行了比较,并提出了ResNet18-CL模型.
主要成果:
- 在LLAB评分方面,ResNet18表现出了比传统的机器学习方法更高的性能.
- 与ResNet18相比,拟议的ResNet18-CL模型取得了显著的改进:精度和回忆率为5.98%,精度为6.58%,F1得分为7.36%,与ResNet18相比.
- 该模型有效地区分了不同的LLAB严重程度.
结论:
- 基于摄像头的足部 perfusion 分析是 LLAB 评估的一个可行的方法.
- ResNet18-CL模型为LLAB严重程度分级提供了一个及时,经济有效和高效的解决方案.
- 这种方法对早期PAD预防和管理具有前途.
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