背景:炎症性皮肤病,比如牛皮癣,特应性湿疹,和接触性皮炎由于其不同的临床表现和快速和精确的诊断评估的需要带来诊断挑战.
目的:虽然最近的研究描述了非侵入性成像设备,如光学相干断层扫描和线场共聚焦OCT(LC-OCT),作为实时可视化病理特征的可能技术,尚未进行标准化分析和验证.
方法:诊断为特应性湿疹的患者的一百四十个病变(57),牛皮癣(50),和接触性皮炎(33)使用OCT和LC-OCT成像。采用统计分析来评估其特征形态特征的重要性。此外,开发了一种基于Gini系数计算的决策树算法,以识别关键属性和准确分类疾病组的标准。
结果:描述性统计揭示了湿疹的独特形态特征,牛皮癣,和接触性皮炎病变。多变量逻辑回归证明了这些特征的重要性,提供了三种炎症状态之间的强大区别。决策树算法通过识别疾病判别的最佳属性,进一步提高了分类精度,强调特定的形态学标准对临床快速诊断至关重要。
结论:描述性统计的组合方法,多元逻辑回归,决策树算法提供了与每个炎症性皮肤病相关的独特方面的透彻理解。这项研究为病变分类提供了一个实用的框架,增强临床医生对成像结果的可解释性。
BACKGROUND: Inflammatory skin diseases, such as psoriasis, atopic eczema, and contact dermatitis pose diagnostic challenges due to their diverse clinical presentations and the need for rapid and precise diagnostic assessment.
OBJECTIVE: While recent studies described non-invasive imaging devices such as Optical coherence tomography and Line-field confocal OCT (LC-OCT) as possible techniques to enable real-time visualization of pathological features, a standardized analysis and validation has not yet been performed.
METHODS: One hundred forty lesions from patients diagnosed with atopic eczema (57), psoriasis (50), and contact dermatitis (33) were imaged using OCT and LC-OCT. Statistical analysis was employed to assess the significance of their characteristic morphologic features. Additionally, a decision tree algorithm based on Gini\'s coefficient calculations was developed to identify key attributes and criteria for accurately classifying the disease groups.
RESULTS: Descriptive statistics revealed distinct morphologic features in eczema, psoriasis, and contact dermatitis lesions. Multivariate logistic regression demonstrated the significance of these features, providing a robust differentiation between the three inflammatory conditions. The decision tree algorithm further enhanced classification accuracy by identifying optimal attributes for disease discrimination, highlighting specific morphologic criteria as crucial for rapid diagnosis in the clinical setting.
CONCLUSIONS: The combined approach of descriptive statistics, multivariate logistic regression, and a decision tree algorithm provides a thorough understanding of the unique aspects associated with each inflammatory skin disease. This research offers a practical framework for lesion classification, enhancing the interpretability of imaging results for clinicians.