Deep neural networks are superior to dermatologists in melanoma image classification

• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Verfasst von: Enk, Alexander [VerfasserIn]
• Verfasst von: Holland-Letz, Tim [VerfasserIn]
• Verfasst von: Utikal, Jochen [VerfasserIn]
• Titel: Deep neural networks are superior to dermatologists in melanoma image classification
• Verf.angabe: Titus J. Brinker, Achim Hekler, Alexander H. Enk, Carola Berking, Sebastian Haferkamp, Axel Hauschild, Michael Weichenthal, Joachim Klode, Dirk Schadendorf, Tim Holland-Letz, Christof von Kalle, Stefan Fröhling, Bastian Schilling, Jochen S. Utikal
• E-Jahr: 2019
• Jahr: 8 August 2019
• Umfang: 7 S.
• Fussnoten: Gesehen am 06.11.2019
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2019
• Band/Heft Quelle: 119(2019), Seite 11-17
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2019.05.023
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Artificial intelligence
• Sach-SW: Deep learning
• Sach-SW: Melanoma
• Sach-SW: Skin cancer
• K10plus-PPN: 168115319X

Abstract

Background - Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date. - Methods - For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes. - Findings - The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%-71.7%) and 62.2% (95% CI: 57.6%-66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%-85.7%) and a higher specificity of 77.9% (95% CI: 73.8%-81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups. - Interpretation - For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001).