Investigating the clinical implication of corneometer and mexameter readings towards objective, efficient evaluation of psoriasis vulgaris severity

Abstract

In clinical settings, although Psoriasis Area and Severity Index (PASI) scoring system can provide a quick visual assessment of the severity of psoriasis vulgaris, there is still a strong demand for higher efficiency and accuracy in quantifying the inflammation status of psoriatic lesions. Currently, there are already commercial systems, such as the Courage + Khazaka Corneometer and Mexameter that measure skin capacitance and optical reflectance, for conveniently quantifying the status of skin barrier function and erythema of skin. Despite numerous comparisons of the Courage + Khazaka system with the PASI scoring system, they are rarely compared on parity with diffuse reflectance spectroscopy (DRS) based systems. In this study, we employed a custom-built DRS system shown to be able to determine the skin water-protein binding status and the hemoglobin concentration, and we performed cross-validation of the DRS measurement results with the readings derived from the Corneometer and Mexameter as well as a portion of the PASI scores. Our results revealed that the erythema readings from the Mexameter were a good representation of skin oxygenated hemoglobin but not the deoxygenated hemoglobin. On the other hand, the dermatologists recruited in this study were inclined to rate higher scores on the “erythema” category as skin’s deoxygenated hemoglobin level was higher. Thus, the Mexameter derived erythema readings may not be coherent with the PASI erythema scores. Further, the Corneometer derived skin capacitance readings were well correlated to the PASI “desquamation” and “thickness” scores, while the PASI “desquamation” evaluation was a dominating factor contributing to the DRS deduced water-protein binding status. We conclude that the DRS method could be a valuable addition to existing skin capacitance/reflectance measurement systems and the PASI scoring system toward achieving a more efficient and objective clinical psoriasis vulgaris severity evaluation.

Psoriasis severity classification based on adaptive multi-scale features for multi-severity disease

“…Many studies have been conducted using skin erythema and hydration meters 7 , traditional conventional methods, and state-of-the-art (SOTA) machine learning and deep learning technologies to objectify the PASI score, which plays an important role in psoriasis diagnosis and treatment evaluation. First, erythema and scaling, which are psoriasis lesions, have distinct color differences according to severity.…”