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一种基于皮肤电导得出的、用于评估皮肤表面微血管病变的临床可解释模型。

A clinically interpretable model derived from skin conductance for assessing microangiopathy of the skin surface.

作者信息

Zhu Ximing, Chu ShihChun, Fu Jie, Ao Siyu, Liu Tingting, Liao Ziming, Song Jiamei, Wang Yuhong, Guo Yanping, Song Qinghong, Liu Pengfei, Li Shuang, Chen Bing, Yao Bin

机构信息

Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, PR China.

Department of Vascular Surgery, The Second Affiliated Hospital of Zhejiang University's Medical College, Hangzhou, 310051, China.

出版信息

NPJ Digit Med. 2025 Mar 28;8(1):181. doi: 10.1038/s41746-025-01562-w.

Abstract

The electrical properties of the skin can reflect changes in its structure and physiological state, and bioimpedance analysis has been widely used to distinguish specific components of the human body, such as water, fat, or muscle tissue, instead of the traditional examinations with bulky equipment gradually. For the assessment of microangiopathy, a low-cost, simple, effective, and clinically interpretable model was proposed that relies on individual skin conductance data, collected from 28 patients with lower extremity arterial occlusion. The model demonstrated a specificity of 67.4% and a sensitivity of 82.9% in classifying healthy-affected sides. The severity estimates were consistent with the patient's laser speckle and ankle-brachial index, with intra-class correlation coefficients of 0.43 and 0.55. Further, patient record data was combined to improve accuracy by about 15% in multimodal ensemble learning, indicating the potential for using the electrical properties of the skin to characterize surface microcirculation disorders.

摘要

皮肤的电学特性能够反映其结构和生理状态的变化,生物阻抗分析已逐渐被广泛应用于区分人体的特定成分,如水、脂肪或肌肉组织,而非传统的使用笨重设备进行的检查。为了评估微血管病变,提出了一种低成本、简单、有效且具有临床可解释性的模型,该模型依赖于从28例下肢动脉闭塞患者收集的个体皮肤电导数据。在对健康侧和患病侧进行分类时,该模型的特异性为67.4%,敏感性为82.9%。严重程度估计与患者的激光散斑和踝臂指数一致,组内相关系数分别为0.43和0.55。此外,在多模态集成学习中,结合患者记录数据可将准确率提高约15%,这表明利用皮肤的电学特性来表征表面微循环障碍具有潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa56/11953216/764d7ad1839c/41746_2025_1562_Fig1_HTML.jpg

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