Foolad Negar, Prakash Neha, Shi Vivian Y, Kamangar Faranak, Wang Qinlu, Li Chin-Shang, Sivamani Raja K
Department of Dermatology, University of California - Davis, Sacramento, CA, USA.
Department of Statistics, University of California - Davis, Davis, CA, USA.
J Cosmet Dermatol. 2016 Mar;15(1):43-8. doi: 10.1111/jocd.12191. Epub 2015 Nov 4.
The reproducible evaluation of facial redness is critical to the assessment of erythematotelangiectatic rosacea. Assessments have typically focused on the use of photography with the use of semi-quantitative grading scales based on evaluator rating. However, few studies have utilized computer-based algorithms to evaluate facial redness.
The purpose of this clinical study was to assess whether there is correlation between clinical grading of facial redness to the assessment of a quantitative computer-based facial modeling and measurement.
In this prospective study, a set of high-resolution facial photographs and cross-polarized subsurface photographs for erythema detection were obtained for 31 study participants. A computer algorithm was then utilized to detect and quantify facial redness in the photographs and compare this to semi-quantitative evaluator-based grading for facial redness.
There was a strong correlation between computer-based cross-polarized subsurface erythema quantification and clinical grading for redness intensity (Clinical Erythema Assessment), redness distribution, and overall redness severity (Modified Clinical Erythema Assessment).
Overall, facial redness measurements by facial imaging and computer analysis correlated well to clinical grading scales for both redness intensity and distribution. Future studies should incorporate facial modeling and analysis tools for assessments in clinical studies to introduce greater objectivity and quantitative analysis in facial erythema-based analyses.
对面部发红进行可重复性评估对于红斑毛细血管扩张型酒渣鼻的评估至关重要。评估通常集中于使用摄影技术，并基于评估者评分采用半定量分级量表。然而，很少有研究利用基于计算机的算法来评估面部发红情况。
本临床研究的目的是评估面部发红的临床分级与基于计算机的面部建模和测量评估之间是否存在相关性。
在这项前瞻性研究中，为31名研究参与者获取了一组用于红斑检测的高分辨率面部照片和交叉偏振皮下照片。然后利用一种计算机算法来检测和量化照片中的面部发红情况，并将其与基于评估者的面部发红半定量分级进行比较。
基于计算机的交叉偏振皮下红斑量化与发红强度（临床红斑评估）、发红分布和整体发红严重程度（改良临床红斑评估）的临床分级之间存在很强的相关性。
总体而言，通过面部成像和计算机分析进行的面部发红测量与发红强度和分布的临床分级量表相关性良好。未来的研究应在临床研究评估中纳入面部建模和分析工具，以便在基于面部红斑的分析中引入更高的客观性和定量分析。
