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

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dc.contributor.authorMunoz-Lopez, C.
dc.contributor.authorRamirez-Cornejo, C.
dc.contributor.authorMarchetti, M. A.
dc.contributor.authorHan, S. S.
dc.contributor.authorDel Barrio-Diaz, P.
dc.contributor.authorJaque, A.
dc.contributor.authorUribe, P.
dc.contributor.authorMajerson, D.
dc.contributor.authorCuri, M.
dc.contributor.authorDel Puerto, C.
dc.contributor.authorReyes-Baraona, F.
dc.contributor.authorMeza-Romero, R.
dc.contributor.authorParra-Cares, J.
dc.contributor.authorAraneda-Ortega, P.
dc.contributor.authorGuzman, M.
dc.contributor.authorMillan-Apablaza, R.
dc.contributor.authorNunez-Mora, M.
dc.contributor.authorLiopyris, K.
dc.contributor.authorVera-Kellet, C.
dc.contributor.authorNavarrete-Dechent, C.
dc.date.accessioned2025-01-20T23:55:58Z
dc.date.available2025-01-20T23:55:58Z
dc.date.issued2021
dc.description.abstractBackground 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.
dc.description.abstractObjective To assess the diagnostic performance and potential clinical utility of a 174-multiclass AI algorithm in a real-life telemedicine setting.
dc.description.abstractMethods 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.
dc.description.abstractResults 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.
dc.description.abstractConclusions 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.
dc.description.funderMSKCC institutional NIH/NCI Cancer Center Support Grant
dc.fuente.origenWOS
dc.identifier.doi10.1111/jdv.16979
dc.identifier.eissn1468-3083
dc.identifier.issn0926-9959
dc.identifier.urihttps://doi.org/10.1111/jdv.16979
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/95123
dc.identifier.wosidWOS:000591154500001
dc.issue.numero2
dc.language.isoen
dc.pagina.final553
dc.pagina.inicio546
dc.revistaJournal of the european academy of dermatology and venereology
dc.rightsacceso restringido
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titlePerformance of a deep neural network in teledermatology: a single-centre prospective diagnostic study
dc.typeartículo
dc.volumen35
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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