结合热学和人工智能与糖尿病足护士相比,用于糖尿病足的检测:一个诊断准确性研究
Khansa Shara1, Mustafa Alghali2, Waseem Abu-Ashour3,4
1Nursing Department, Johns Hopkins Aramco Healthcare, Dhahran, Kingdom of Saudi Arabia.
Digital health
|January 22, 2026
概括
使用热成像的人工智能 (AI) 工具在检测糖尿病脚并发症方面显示出高准确度,与护士的评估相匹配. 这种人工智能为早期风险识别提供了可扩展的解决方案,有可能预防和截肢.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 糖尿病学 糖尿病学
背景情况:
- 早期发现糖尿病足部并发症至关重要,以预防和截肢等严重后果.
- 热成像是一种非侵入性的风险识别方法,传统上需要专家的人类解释.
- 人工智能 (AI) 提供了一个更具可扩展性和客观性解释热图数据的潜力.
研究的目的:
- 评估人工智能驱动的热图查工具的诊断性能.
- 将人工智能工具对糖尿病脚部并发症的风险识别与护士领导的临床评估进行比较.
主要方法:
- 一项横截面研究涉及100名患有糖尿病的成年人,他们接受了例行脚部查.
- 基于智能手机的热成像捕获了脚部图像,人工智能模型生成了风险分数 (0-3).
- 一名糖尿病足护士进行了临床检查,以获得参考风险评分,诊断准确度根据这个标准计算.
主要成果:
- 在中高风险病例中,人工智能系统实现了100%的灵敏度和96.8%的特异性.
- 在AI产生的分数和护士评估之间观察到强烈的相关性 (ρ = 0.973).
- 人工智能和护士评估都表明,温度不对称性增加,风险水平更高.
结论:
- 人工智能模型表现出高准确度,成功识别了护士标记的所有中高风险病例.
- 人工智能工具与热量数据的调整和一致的得分强调了它作为糖尿病足查的可扩展辅助工具的潜力.
- 纵向研究的进一步验证可以支持将这种AI工具集成到远程和初级保健机构.
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