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

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Ximing Zhu, ShihChun Chu, Jie Fu, Siyu Ao, Tingting Liu, Ziming Liao, Jiamei Song, Yuhong Wang, Yanping Guo, Qinghong Song, Pengfei Liu, Shuang Li, Bing Chen, Bin Yao
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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.

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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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