Physician Level Assessment of Hirsute Women and of Their Eligibility for Laser Treatment With Deep Learning
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.
Authors
- Raluca Jalaboi (ORCID: https://orcid.org/0000-0001-6269-0527)
- Henrik Lorentzen (ORCID: https://orcid.org/0000-0001-8442-3569)
- Sanne Frost
- Sofie E. Jørgensen (ORCID: https://orcid.org/0000-0002-2700-0882)
- Lars Iversen (ORCID: https://orcid.org/0000-0003-1816-4508)
- Kenneth Thomsen (ORCID: https://orcid.org/0000-0002-2337-7746)
- Trine Bertelsen (ORCID: https://orcid.org/0000-0002-4777-1991)
- Lene Hedelund
- Trine Høgsberg
- Ole Winther (ORCID: https://orcid.org/0000-0002-1966-3205)
- Henrik Egekvist
- Hans B. Lomholt
Institutions
- University of Copenhagen (DK)
- Aarhus University (DK)
- Odense University Hospital (DK)
- Aarhus University Hospital (DK)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
- Hudklinikken (DK)
- Aalborg University (DK)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Lasers in Surgery and Medicine
- Published
- 2024-09-22
- DOI
- https://doi.org/10.1002/lsm.23843
- Citations
- 1
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.54