School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Clin Rheumatol. 2019 Sep;38(9):2343-2354. doi: 10.1007/s10067-019-04644-9. Epub 2019 Jul 5.
Nailfold capillaroscopy (NC) is a highly sensitive, safe, and non-invasive technique to assess involvement rate of microvascularity in dermatomyositis and systemic sclerosis. A large number of studies have focused on NC pattern description, classification, and scoring system validation, but minimal information has been published on the accuracy and precision of the measurement. The objective of this review article is to identify different factors affecting the reliability and validity of the assessment in NC. Several factors can affect the reliability of the examination, e.g., physiological artifacts, the nailfold imaging instrument, human factors, and the assessment rules and standards. It is impossible to avoid all artifacts, e.g., skin transparency, physically injured fingers, and skin pigmentation. However, minimization of the impact of some of these artifacts by considering some protocols before the examination and by using specialized tools, training, guidelines, and software can help to reduce errors in the measurement and assessment of NC images. Establishing guidelines and instructions for automatic characterization and measurement based on machine learning techniques also may reduce ambiguities and the assessment time.
甲褶毛细血管镜检查(NC)是一种高度敏感、安全、非侵入性的技术,可评估皮肌炎和系统性硬化症中小血管受累的发生率。大量研究集中于 NC 模式描述、分类和评分系统验证,但关于测量的准确性和精密度的信息很少。本文的目的是确定影响 NC 评估可靠性和有效性的不同因素。
一些因素会影响检查的可靠性,例如,生理伪影、甲褶成像仪器、人为因素以及评估规则和标准。不可能避免所有的伪影,例如,皮肤透明度、手指物理损伤和皮肤色素沉着。然而,通过在检查前考虑一些方案,并使用专用工具、培训、指南和软件,可尽量减少这些伪影的影响,从而减少 NC 图像测量和评估中的误差。
基于机器学习技术为自动特征描述和测量建立准则和说明,也可能减少歧义并缩短评估时间。