Περίληψη:
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 tested in real-life conditions. Objective: To assess the diagnostic performance and potential clinical utility of a 174-multiclass 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 and 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 11.8% 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 photographs via telemedicine. © 2020 European Academy of Dermatology and Venereology
Συγγραφείς:
Muñoz-López, C.
Ramírez-Cornejo, C.
Marchetti, M.A.
Han, S.S.
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.
Λέξεις-κλειδιά:
adult; Article; artificial intelligence; clinical assessment; clinical evaluation; controlled study; deep neural network; diagnostic accuracy; diagnostic test accuracy study; exposure; female; human; image quality; imaging algorithm; major clinical study; male; priority journal; prospective study; skin defect; teleconsultation; teledermatology; dermatology; skin disease; telemedicine, Adult; Artificial Intelligence; Dermatology; Female; Humans; Male; Neural Networks, Computer; Prospective Studies; Skin Diseases; Telemedicine
