Titus J Brinker1, Achim Hekler2, Alexander H Enk3, Carola Berking4, Sebastian Haferkamp5, Axel Hauschild6, Michael Weichenthal6, Joachim Klode7, Dirk Schadendorf7, Tim Holland-Letz8, Christof von Kalle2, Stefan Fröhling2, Bastian Schilling9, Jochen S Utikal10. 1. National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany. Electronic address: titus.brinker@dkfz.de. 2. National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany. 3. Department of Dermatology, University Hospital Heidelberg, Heidelberg, Germany. 4. Department of Dermatology, University Hospital Munich (LMU), Munich, Germany. 5. Department of Dermatology, University Hospital Regensburg, Regensburg, Germany. 6. Department of Dermatology, University Hospital Kiel, Kiel, Germany. 7. Department of Dermatology, University Hospital Essen, Essen, Germany. 8. Department of Biostatistics, German Cancer Research Center, Heidelberg, Germany. 9. Department of Dermatology, University Hospital Würzburg, Würzburg, Germany. 10. Department of Dermatology, Heidelberg University, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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).
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).
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