A novel multi-layer perceptron model for assessing the diagnostic value of non-invasive imaging instruments for rosacea

Abstract

Background Reflectance confocal microscopy (RCM), VISIA, and dermoscopy have emerged as promising tools for objective diagnosis and assessment of rosacea. However, little is known about the diagnostic value of these imaging systems for rosacea. Objectives To assess the diagnostic value of RCM, VISIA, and dermoscopy for rosacea by establishing a novel multilayer perceptron (MLP) model. Methods A total of 520 patients with rosacea and other facial diseases were included in this study. A total of 474 samples of dermoscopy data, 374 samples of RCM data, 434 samples of VISIA data, and 291 samples containing three data sources were collected. An MLP model was built with the total data to explore the association between the imageological features of each instrument and the probability of rosacea. Results Our MLP model revealed that the area under the receiver operating characteristic curve (AUROC) values of RCM, VISIA and dermoscopy for diagnosing rosacea were 0.5233, 0.5646 and 0.7971, respectively. The integration of these three tools with clinical data could further improve the accuracy of the predictive diagnosis to 0.8385. For the imageological features of each tool, abnormalities (hyperkeratosis or parakeratosis) in the stratum corneum were effective variables for excluding rosacea (odds ratio [OR], 0.4333) under RCM. The indicators of rosacea under VISIA included overall severity of erythema, erythema involving the cheek or superciliary arch, visible red blood vessels, and papules (OR = 2.2745, 3.1592, 1.8365, 2.8647, and 1.4260, respectively). The candidate variables of dermoscopy included yellow background, white background, uniform distribution of vessels, branched vessels, and reticular blood vessels (OR = 0.4259, 0.4949, 2.2858, 3.7444, and 2.4576, respectively). Conclusions RCM, dermoscopy, and VISIA each can present several imageological features and were of certain value for assisting rosacea diagnosis. The combined analysis of these three tools using our MLP model may be useful for improving the accuracy of diagnosing rosacea.