Journal of cosmetic dermatology · 2025
Artificial Intelligence Predicts Fitzpatrick Skin Type, Pigmentation, Redness, and Wrinkle Severity From Color Photographs of the Face.
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Draelos Rachel L, Kesty Chelsea E, Kesty Katarina R
Аннотация
BACKGROUND: Due to high patient demand, increasing numbers of non-dermatologists are performing skin assessments and carrying out laser interventions in medical spas, leading to inferior outcomes and higher complications. A machine learning tool that automatically analyzes patient skin has the potential to aid non-dermatologists.
AIMS: To develop a high-performing machine learning model that predicts Fitzpatrick skin type, hyperpigmentation, redness, and wrinkle severity simultaneously.
METHODS: We developed the SkinAnalysis dataset of 3662 images, labeled by a dermatologist across five skin scales. We trained and evaluated machine learning models across 15 different configurations, including three neural network architectures and two loss functions.
RESULTS: The best-performing model was an EfficientNet-V2M architecture with a custom cross entropy loss. This model's mean test set accuracy across all labels was 85.41 ± 9.86 and its mean test set AUROC was 0.8306 ± 0.09599. An interesting trend emerged in which machine learning model performance was higher at the extremes of the scales, suggesting greater clinical ambiguity in the middle of the scales.
CONCLUSIONS: Machine learning models are capable of predicting multiple skin characteristics simultaneously from color photographs of the face. In the future, similar models could assist non-dermatologists in patient skin evaluation to enhance treatment planning.
AIMS: To develop a high-performing machine learning model that predicts Fitzpatrick skin type, hyperpigmentation, redness, and wrinkle severity simultaneously.
METHODS: We developed the SkinAnalysis dataset of 3662 images, labeled by a dermatologist across five skin scales. We trained and evaluated machine learning models across 15 different configurations, including three neural network architectures and two loss functions.
RESULTS: The best-performing model was an EfficientNet-V2M architecture with a custom cross entropy loss. This model's mean test set accuracy across all labels was 85.41 ± 9.86 and its mean test set AUROC was 0.8306 ± 0.09599. An interesting trend emerged in which machine learning model performance was higher at the extremes of the scales, suggesting greater clinical ambiguity in the middle of the scales.
CONCLUSIONS: Machine learning models are capable of predicting multiple skin characteristics simultaneously from color photographs of the face. In the future, similar models could assist non-dermatologists in patient skin evaluation to enhance treatment planning.