Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists

Dong Hyo Kim, Sukkyu Sun, Soo Ick Cho, Hyoun Joong Kong, Ji Won Lee, Jun Hyo Lee, Dae Hun Suh

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation. Objectives: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test. Methods: A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions. Results: In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson’s correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation. Conclusions: Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.

Original languageEnglish
Pages (from-to)649-659
Number of pages11
JournalAmerican Journal of Clinical Dermatology
Volume24
Issue number4
DOIs
StatePublished - Jul 2023

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