Pilot study of an automated method to determine Melasma Area and Severity Index.

BACKGROUND

Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter-observer variability.

OBJECTIVES

To develop and validate a novel image analysis software designed to automatically calculate the area and degree of hyperpigmentation in melasma from computer image analysis of whole-face digital photographs, thereby deriving an automated mMASI score (aMASI).

METHODS

The algorithm was developed in collaboration between dermatologists and image analysis experts. Firstly, using an adaptive threshold method, the algorithm identifies, segments and calculates the areas involved. It then calculates the darkness. Finally, the derived area and darkness are then used to calculate mMASI. The scores derived from the algorithm are validated prospectively. Twenty-nine patients with melasma using depigmenting agents were recruited for validation. Three dermatologists scored mMASI at baseline and post-treatment using standardized photographs. These scores were compared with aMASI scores derived from computer analysis.

RESULTS

aMASI scores correlated well with clinical mMASI pre-treatment (r = 0·735, P < 0·001) and post-treatment (r = 0·608, P < 0·001). aMASI was reliable in detecting changes with treatment. These changes in aMASI scores correlated well with changes in clinician-assessed mMASI (r = 0·622, P < 0·001).

CONCLUSIONS

This study proposes a novel approach in melasma scoring using digital image analysis. It holds promise as a tool that would enable clinicians worldwide to standardize melasma severity scoring and outcome measures in an easy and reproducible manner, enabling different treatment options to be compared accurately.