Physician Level Assessment of Hirsute Women and of Their Eligibility for Laser Treatment With Deep Learning

OBJECTIVES: Hirsutism is a widespread condition affecting 5%-15% of females. Laser treatment of hirsutism has the best long-term effect. Patients with nonpigmented or nonterminal hairs are not eligible for laser treatment, and the current patient journey needed to establish eligibility for laser hair removal is problematic in many health-care systems.

METHODS: In this study, we compared the ability to assess eligibility for laser hair removal of health-care professionals and convolutional neural network (CNN)-based models.

RESULTS: The CNN ensemble model, synthesized from the outputs of five individual CNN models, reached an eligibility assessment accuracy of 0.52 (95% CI: 0.42-0.60) and a κ of 0.20 (95% CI: 0.13-0.27), taking a consensus expert label as reference. For comparison, board-certified dermatologists achieved a mean accuracy of 0.48 (95% CI: 0.44-0.52) and a mean κ of 0.26 (95% CI: 0.22-0.31). Intra-rater analysis of board-certified dermatologists yielded κ in the 0.32 (95% CI: 0.24-0.40) and 0.65 (95% CI: 0.56-0.74) range.

CONCLUSION: Current assessment of eligibility for laser hair removal is challenging. Developing a laser hair removal eligibility assessment tool based on deep learning that performs on a par with trained dermatologists is feasible. Such a model may potentially reduce workload, increase quality and effectiveness, and facilitate equal health-care access. However, to achieve true clinical generalizability, prospective randomized clinical intervention studies are needed.