Identifying diagnostic biomarkers for Erythemato-Squamous diseases using explainable machine learning

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Zheng Wang , Li Chang , Tong Shi , Hui Hu , Chong Wang , Kaibin Lin , Jianglin Zhang
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Abstract

Erythemato-squamous diseases (ESD) are a heterogeneous group encompassing six clinically and histopathologically overlapping subtypes, representing a substantial diagnostic challenge within dermatology. The existing body of research reveals a notable void in detailed examinations that deconvolute the distinct features endemic to each ESD variant. To bridge this knowledge gap, our study applied Explainable Artificial Intelligence (XAI) techniques to systematically elucidate the intricate diagnostic biomarker profiles unique to each ESD category. Methodological rigor was fortified through the employment of stratified cross-validation, bolstering the robustness and generalizability of our diagnostic model. The CatBoost classifier emerged as a preeminent algorithm within our analytical framework, manifesting exemplary classification prowess with an accuracy of 99.07%, precision of 99.12%, recall of 98.89%, and an F1 score of 98.97%. Central to our inquiry was the deployment of Shapley Additive exPlanations (SHAP) values, which afforded granular insight into the contributory weight of individual diagnostic biomarkers for each ESD subtype. Our findings delineated pivotal diagnostic biomarkers including saw-tooth appearance of retes (STAR), melanin incontinence (MI), vacuolisation and damage of basal layer (VDBL), polygonal papules (PP), and band-like infiltrate (BLI) as instrumental in the identification of seborrheic dermatitis, while Psoriasis was characterized by fibrosis of the papillary dermis (FPD), thinning of the suprapapillary epidermis (TSE), elongation of the rete ridges (ERR), clubbing of the rete ridges (CRR), and notable psoriatic spongiosis. This integrative approach, leveraging the analytical acumen of Random Forest coupled with the interpretability afforded by SHAP, signifies a significant advancement in the nuanced diagnostic landscape of ESD.
利用可解释的机器学习识别红斑狼疮的诊断生物标志物
红斑鳞状上皮内瘤病(ESD)是一种异质性疾病,包括六种在临床和组织病理学上相互重叠的亚型,是皮肤病学诊断中的一大难题。现有的研究表明,在详细检查以确定每种 ESD 变体的独特特征方面存在明显的空白。为了弥补这一知识空白,我们的研究应用了可解释人工智能(XAI)技术,系统地阐明了每个 ESD 类别所特有的复杂的诊断生物标志物特征。通过分层交叉验证加强了方法的严谨性,从而提高了诊断模型的稳健性和普适性。在我们的分析框架中,CatBoost 分类器是一种杰出的算法,它的分类能力堪称典范,准确率为 99.07%,精确率为 99.12%,召回率为 98.89%,F1 分数为 98.97%。我们研究的核心是利用沙普利加性前平面图(SHAP)值,该值可让我们深入了解每种 ESD 亚型的单个诊断生物标志物的贡献权重。我们的研究结果确定了一些关键的诊断生物标志物,包括锯齿状网状外观(STAR)、黑色素失禁(MI)、空泡化和基底层损伤(VDBL)、多角形丘疹(PP)和带状浸润(BLI),它们在脂溢性皮炎的鉴别中起着重要作用、而银屑病的特征则是乳头状真皮纤维化(FPD)、乳头上表皮变薄(TSE)、齿状嵴伸长(ERR)、齿状嵴俱乐部化(CRR)和明显的银屑病海绵状增生。这种综合方法利用随机森林(Random Forest)的敏锐分析能力和 SHAP 的可解释性,标志着 ESD 细微诊断领域的重大进步。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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