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一个印度数据库用于分级伤口愈合和跨体分类,使用基于扰乱的可解释AI模型

Naveen Varghese Jacob¹, Sowmya V¹, Gopalakrishnan E A²

¹Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India.

Computer methods and programs in biomedicine

|August 5, 2025

概括

这项研究开发了一个深度学习系统,使用智能手机图像来分类伤口愈合阶段,达到80%以上的准确性. 这个人工智能工具帮助患者和医生在伤口护理管理.

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:

长时间住院和频繁的随访会给受伤患者和医疗保健提供者带来巨大的负担.
目前的伤口评估方法可能是主观的,耗时的,影响患者的护理和资源分配.

研究的目的:

用轻量级深度学习 (DL) 模型和2D RGB智能手机图像来分类伤口愈合阶段.
通过实现远程伤口评估,减少患者的财务和心理健康压力.
帮助医疗保健专业人员优先考虑患者护理和更有效地管理工作量.

主要方法:

利用新的印度伤口数据集 (Amrita_wound) 和开放数据集 (AZH,Medetec) 来进行多类伤口分类.
采用了十个轻量级DL模型与转移学习,增强了特征扰动以改善预测.
集成的跨体分析用于概括性评估和Grad-CAM用于可解释AI (XAI),以确保预测透明度.

主要成果:

在Amrita_wound数据集上的MobileViT X S和FastViT T8模型中,在所有等级上实现了超过80%的精度,F1分数,特异性和灵敏度.
移动ViT X S型号表现出卓越的性能,平均灵敏度为90%.
对未见数据的跨体分析 (Medetec) 平均F1得分为68%,而3级伤口的F1得分为77%.

结论:

使用人工智能的自动伤口分级系统可以使患者能够了解愈合进展,并帮助临床医生优先考虑护理.
可解释AI (Grad-CAM) 集成通过验证预测完整性,在患者,医生和AI模型之间建立信任.

关键词:

跨体的身体.可解释的人工智能轻量级的模型轻量级的模型移动视觉变压器移动视觉变压器伤口分级伤口分级伤口分级伤口愈合伤口愈合

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