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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017 - 11/02/20

Doi : 10.1016/j.jaad.2019.07.016 
Michael A. Marchetti, MD a, , Konstantinos Liopyris, MD a, Stephen W. Dusza, DrPH a, Noel C.F. Codella, PhD b, David A. Gutman, MD, PhD c, d, e, Brian Helba, BS f, Aadi Kalloo, MHS a, Allan C. Halpern, MD a
for the

International Skin Imaging Collaboration

H. Peter Soyer, MD, Clara Curiel-Lewandrowski, MD, Liam Caffery, PhD, Josep Malvehy, MD

a Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 
b IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York 
c Department of Neurology, Emory University School of Medicine, Atlanta, Georgia 
d Department of Psychiatry, Emory University School of Medicine, Atlanta, Georgia 
e Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia 
f Kitware Inc, Clifton Park, New York 

Reprint requests: Michael A. Marchetti, MD, Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, 16 East 60th St, New York, NY 10022.Department of MedicineMemorial Sloan Kettering Cancer Center16 East 60th StNew YorkNY10022

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.

Le texte complet de cet article est disponible en PDF.

Key words : automated melanoma diagnosis, computer algorithm, computer vision, deep learning, dermatologist, International Skin Imaging Collaboration, International Symposium on Biomedical Imaging, machine learning, melanoma, reader study, skin cancer

Abbreviations used : CI, ISBI, ISIC, ROC, SK


Plan


 Funding sources: Supported in part through the National Institutes of Health, National Cancer Institute, Cancer Center Support Grant P30 CA008748.
 Conflicts of interest: Dr Codella is an employee of IBM and an IBM stockholder. Dr Halpern is a consultant for Canfield Scientific Inc, Caliber ID, and SciBase. Dr Marchetti, Dr Liopyris, Dr Dusza, Dr Gutman, Mr Helba, and Mr Kalloo have no financial conflicts of interest to disclose.
 Previously presented preliminary study data at the World Congress of Melanoma in Brisbane, Australia, in October 2017.


© 2019  American Academy of Dermatology, Inc.. Publié par Elsevier Masson SAS. Tous droits réservés.
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Vol 82 - N° 3

P. 622-627 - mars 2020 Retour au numéro
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