ArticleAmerican journal of clinical dermatology2023
Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists.
Article in American journal of clinical dermatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence-assisted identification of skin lesions associated with chronic venous insufficiency.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
- Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review.Journal of personalized medicine · 2025Review
- Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.Scientific reports · 2025Article
- Transgender Individuals' Perspectives on Teledermatology-Based Acne Research.Archives of dermatological research · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation.
objectivesWe 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.
methodsA 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.
resultsIn 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.
conclusionsOverall, 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.
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