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人工智能增强成像用于糖尿病足风险评估和诊断:一项回顾性队列研究

Tilak Bhattacharya¹, Sandip Chakraborty², Ghanshyam Goyal³

¹University of Engineering and Management, Kolkata, India.

Journal of diabetes science and technology

|January 16, 2026

概括

这项研究开发了一种人工智能方法,用于糖尿病足 (DFU) 的细分和严重程度分类. 人工智能在识别DFU边界和分级严重程度方面取得了很高的准确性,改善了临床管理.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算生物学计算生物学

背景情况:

糖尿病足 (DFU) 评估需要精确的细分来准确预测严重程度.
现有的细分工具往往缺乏有效的伤口边界划分所需的准确性.
瓦格纳的分级系统是对DFU严重性进行分类的标准.

研究的目的:

开发和评估人工智能 (AI) 驱动的DFU细分和严重程度分类方法.
与传统方法相比,提高DFU边界划分的准确性.
使用人工智能实现DFU严重性的实时远程评估.

主要方法:

一种新的AI驱动的方法,将增强的活跃轮模型与Sobel边缘检测相结合,用于精确的DFU边界分割.
基于瓦格纳的分级系统,开发一种轻量级分类模型来预测DFU的严重程度.
利用印度510名患者的1339张DFU图像 (增加到6579) 的回顾性队列.

主要成果:

细分方法实现了0.99.99的高子相似系数.
该分类模型显示了95.58%的准确性,95.58%的灵敏性和99.16%的特异性.
该方法表现出较低的假阳性率 (0.84%) 和假阴性率 (4.83%),表现优于现有技术.

结论:

拟议的AI方法显著提高了糖尿病足的细分和分类.
这一进步支持改善了DFU的临床决策和管理.
开发的AI驱动的移动应用程序有助于高效的远程DFU评估.

关键词:

移动网络V3-小规模苏醒的操作员运营商.瓦格纳分级系统是瓦格纳的分级系统.活动轮模型的模型.糖尿病足部糖尿病足部细分化细分化的细分化

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