基于深度学习的诊断框架用于脚-手腕指数定义的下肢伤口的周围动脉疾病:与医生进行比较
Ming-Feng Tsai1, Yu-Chang Chu2, Wen-Teng Yao3
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 110, Taiwan; Division of Plastic Surgery, Department of Surgery, Mackay Memorial Hospital, Taipei, 104, Taiwan; Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan; Wound Care Center, MacKay Memorial Hospital, Taipei, 110, Taiwan.
Computer methods and programs in biomedicine
|February 20, 2025
概括
这项研究引入了一个使用卷积神经网络 (CNN) 进行深度学习的框架,用于检测和分类下肢伤口中的外周动脉疾病 (PAD),为医生提供了一个客观的工具.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 血管外科 血管外科
背景情况:
- 在下肢伤口中诊断周围动脉疾病 (PAD) 往往缺乏客观的工具.
- 基于卷积神经网络 (CNN) 的深度学习用于PAD检测尚未得到充分探索.
- 现有的诊断方法可以是主观的或侵入性的.
研究的目的:
- 开发和评估基于CNN的深度学习框架,用于PAD检测,外围动脉封闭性疾病 (PAOD) 检测和PAD分类.
- 为了比较AlexNet,GoogleNet和ResNet101V2算法的PAD特征表征的性能.
- 评估框架的实用性与人类专家 (整形和重建外科医生和全科医生) 的表现相比.
主要方法:
- 实现 AlexNet,GoogleNet 和 ResNet101V2 CNN 模型用于 PAD 分析.
- 采用原始,背景删除和裁剪图像进行模型培训和验证.
- 对模型性能与整形和重建外科医生 (PRS) 和全科医生 (GP) 组进行比较分析.
主要成果:
- 与AlexNet和GoogleNet相比,ResNet101V2在PAD和PAOD检测和分类方面表现略有改善.
- 图像预处理技术 (删除背景,裁剪) 在模型中显示了类似的性能趋势.
- 在PAD/PAOD检测方面,CNN模型的表现优于全科医生,而PRS的表现与模型相美.
结论:
- 基于CNN的深度学习框架提供了一个有希望的,客观的,可靠的计算工具,用于PAD检测,PAOD检测和下肢伤口中的PAD分类.
- 该框架整合了客观的脚-手臂指数 (ABI) 值和图像预处理以进行增强的表征.
- 这种方法为医生提供了一个易于实施的工具,用于自动识别和分类PAD.
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