Johnson & Johnson Santé Beauté France, Paris, France, France.
Université Côte d'Azur, INRIA, I3S/CNRS, Nice, Antibes, France, France.
J Biomed Opt. 2022 Jul;27(7). doi: 10.1117/1.JBO.27.7.070902.
Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient.
This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images.
A PubMed search was conducted with additional literature obtained from references lists.
The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal-epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images.
RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.
共聚焦激光扫描显微镜（RCM）是一种非侵入性的、活体的技术，可在细胞水平上提供近乎组织病理学的分辨率。它在研究难以获得活检或会对患者造成不必要的组织损伤和创伤的现象时非常有用。
本文综述了 RCM 在皮肤研究中的应用，以及使用机器学习来实现信息自动提取。它有两个目标：（1）概述 RCM 提供的关于皮肤结构的信息，以及皮肤结构如何随时间响应刺激和疾病而变化；（2）概述为自动从 RCM 图像中提取关键形态特征而开发的机器学习方法。
通过 PubMed 进行了搜索，并从参考文献列表中获得了其他文献。
介绍了 RCM 作为皮肤科研究中的活体工具的应用及其衍生的生物学相关信息。综述了用于表皮分层分类、表皮-真皮交界处描绘、皮肤病变分类和图像中单个细胞划分的算法，这些都是皮肤屏障构成的重要因素。由于噪声和/或对比度差，图像分析方法在 RCM 中的应用受到限制。由于 RCM 图像的手动标记既耗时又费力，因此监督机器学习的应用受到限制。
RCM 在皮肤结构研究中具有巨大潜力。人工智能的使用可以使 RCM 图像的研究更加容易、可重复、精确和严格，从而有助于理解皮肤结构、皮肤屏障、皮肤炎症和病变。尽管已经进行了多次尝试，但仍需要进一步的工作，以提供明确的金标准，并解决与图像质量、有限的标记数据集以及现有数据库中表型变异性不足相关的问题。
