Artificial intelligence for the objective evaluation of Acne investigator global assessment

Introduction: The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians. Methods: 479 patients with acne grading ranging from clear to severe and sampled from three ethnic groups participated in this study. Multi-polarization images of facial skin of each patient were acquired from five different angles using the visible spectrum. An Artificial Intelligence was trained using the acquired images to output automatically a measure of Acne severity in the 0-4 numerical range of the Investigator Global Assessment (IGA). Results: The Artificial Intelligence recognized the IGA of a patient with an accuracy of 0.854 and a correlation between manual and automatized evaluation of r=0.958 (P<.001). Discussion: This is the first work where an Artificial Intelligence was able to directly classify acne patients according to an IGA ordinal scale with high accuracy, no human intervention and no need to count lesions.