Performance of a deep neural network in teledermatology: a single-center prospective diagnostic study.

Performance of a deep neural network in teledermatology: a single-center prospective diagnostic study.

J Eur Acad Dermatol Venereol. 2020 Oct 10;:

Authors: Muñoz-López C, Ramírez-Cornejo C, Marchetti MA, Han SS, Del Barrio-Díaz P, Jaque A, Uribe P, Majerson D, Curi M, Del Puerto C, Reyes-Baraona F, Meza-Romero R, Parra-Cares J, Araneda-Ortega P, Guzmán M, Millán-Apablaza R, Nuñez-Mora M, Liopyris K, Vera-Kellet C, Navarrete-Dechent C

Abstract
BACKGROUND: The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not been yet in real-life conditions.
OBJECTIVE: To assess the diagnostic performance and potential clinical utility of an AI algorithm in a real-life telemedicine setting.
METHODS: Prospective, diagnostic accuracy study including consecutive patients who submitted images for teledermatology evaluation. The treating dermatologist chose a single image to upload to a web application during teleconsultation. A follow-up reader study including nine healthcare providers (3 dermatologists, 3 dermatology residents, 3 general practitioners) was performed.
RESULTS: A total of 340 cases from 281 patients met study inclusion criteria. The mean (SD) age of patients was 33.7 (17.5) years; 63% (n=177) were female. Exposure to the AI algorithm results was considered useful in 12% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. The overall top-1 accuracy of the algorithm (41.2%) was lower than that of the dermatologists (60.1%), residents (57.8%) and general practitioners (49.3%) (all comparisons p<0.05, in the reader study). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained, the balanced top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; p=0.049). Algorithm performance was associated with patient skin type and image quality.
CONCLUSIONS: A 174-disease class AI algorithm appears to be a promising tool in the triage and evaluation of lesions with patient-taken photos via telemedicine.

PMID: 33037709 [PubMed - as supplied by publisher]