糖尿病脚诊断的新方法:基于深度学习的检测下肢动脉缩
Chongxin Wu1, Changpeng Xu2, Shuanji Ou2
1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Diabetes research and clinical practice
|December 4, 2023
概括
这项研究引入了一种改进的深度学习模型,用于检测糖尿病足患者的下肢动脉狭窄症 (LEAS). 该模型在识别动脉狭窄方面取得了很高的准确性,有助于更快的临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 血管外科 血管外科
背景情况:
- 糖尿病足 (DFU) 在评估下肢动脉狭窄症 (LEAS) 中带来了挑战.
- 手动LEAS评估是耗时的,主观的,并因血管化和周围组织而复杂.
- 自动化LEAS检测可以加快DFU患者的治疗计划.
研究的目的:
- 开发和评估一个改进的深度学习 (DL) 模型,用于自动检测下肢动脉狭窄症 (LEAS).
- 提高糖尿病足 (DFU) 患者LEAS评估的准确性和效率.
主要方法:
- 从医学图像中重建的3D血管模型用于DL训练.
- 提出了一个改进的基于YOLOv5的模型,包含CBAM,BiFPN和GhostC3模块,用于小目标狭窄检测.
- 利用K-Means++来提高收率和ASIoU损失以实现更快的推理和收.
主要成果:
- 拟议的DL模型实现了85.40%的平均平均精度 (mAP) 检测三种类型的狭窄 (<50%,51%-99%,完全封闭).
- 该模型的处理速度为每秒74.60 (FPS).
- 该研究探讨了DL在糖尿病足患者中用于LEAS检测的应用.
结论:
- 开发的DL模型显示了在DFU患者中精确有效地自动检测LEAS的巨大潜力.
- 这种方法可以帮助临床医生更快地进行诊断和治疗计划.
- 这项研究强调了先进的DL技术在血管疾病的医学图像分析中的有效性.
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