Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
J Am Acad Dermatol
; 82(3): 622-627, 2020 Mar.
Article
de En
| MEDLINE
| ID: mdl-31306724
ABSTRACT
BACKGROUND:
Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.OBJECTIVE:
To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma.METHODS:
In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level.RESULTS:
The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%.LIMITATIONS:
Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata.CONCLUSION:
Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Tumeurs cutanées
/
Interprétation d'images assistée par ordinateur
/
Dermoscopie
/
Apprentissage profond
/
Mélanome
Type d'étude:
Diagnostic_studies
/
Observational_studies
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Pays/Région comme sujet:
America do norte
/
America do sul
/
Asia
/
Colombia
/
Europa
Langue:
En
Journal:
J Am Acad Dermatol
Année:
2020
Type de document:
Article