Measurement of psoriasis-affected area with artificial neural network - 24/02/23
Doi : 10.1016/j.jaad.2022.09.035
Woo Hyup Lee, MD a, Sungmin Lee, PhD b, Jiho Kim, BS c, Ju Hee Han, MD d, Yeong Ho Kim, MD, PhD d, Junhwi Kim, MS e, Ji Hyun Lee, MD, PhD d, Chul Hwan Bang, MD, PhD d, ⁎ 
a Department of Medicine, The Catholic University of Korea College of Medicine, Seocho-gu, Seoul, Korea
b Department of Meta Artificial Intelligence, Corporate R&D Center, SK Telecom, 65, Eulji-ro, Jung-gu, Seoul, Korea
c Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Korea
d Department of Dermatology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seocho-gu, Seoul, Korea
e Department of Machine learning, Steinfeld Co, San Francisco, California
∗Correspondence to: Chul Hwan Bang, MD, PhD, Department of Dermatology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpodaero, Seocho-gu, Seoul, 06591, KoreaDepartment of DermatologySeoul St. Mary's HospitalCollege of MedicineThe Catholic University of Korea222 BanpodaeroSeocho-guSeoul06591Korea
| Drs Woo Hyup Lee and Sungmin Lee contributed equally to this work. |
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| Funding sources: None. |
|
| IRB approval status: Reviewed and approved by the Institutional Review Board of The Catholic University of Korea (IRB: KC21RISI0275). |
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| Key words: artificial intelligence; artificial neural network; psoriasis; psoriasis area and severity index. |
© 2022
American Academy of Dermatology, Inc.. Publié par Elsevier Masson SAS. Tous droits réservés.
Vol 88 - N° 3
P. 731-732 - mars 2023 Retour au numéro
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