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

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.

Keywords: International Skin Imaging Collaboration; International Symposium on Biomedical Imaging; automated melanoma diagnosis; computer algorithm; computer vision; deep learning; dermatologist; machine learning; melanoma; reader study; skin cancer.

Figure 1.. Diagnostic accuracy of the top-ranked algorithm, dermatologists, and residents for melanoma on the 150-image dataset.

Receiver operating characteristic curve demonstrating sensitivity and specificity for melanoma of the top-ranked algorithm from the 2017 ISIC melanoma detection challenge (blue curve). Solid black box indicates the overall performance of 8 dermatologists (center “x”) along with 95% confidence band (rectangular bounding box). Dashed gray box indicates the overall performance of 9 residents (center “x”) along with 95% confidence intervals (rectangular bounding box).