Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017 Journal Article
| Authors: | Marchetti, M. A.; Liopyris, K.; Dusza, S. W.; Codella, N. C. F.; Gutman, D. A.; Helba, B.; Kalloo, A.; Halpern, A. C.; for the International Skin Imaging Collaboration |
| Article Title: | 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. © 2019 American Academy of Dermatology, Inc. |
| Keywords: | major clinical study; cancer diagnosis; diagnostic accuracy; sensitivity and specificity; melanoma; image analysis; skin cancer; epiluminescence microscopy; resident; seborrheic keratosis; cross-sectional study; physician attitude; computer vision; cutaneous melanoma; dermatologist; diagnostic test accuracy study; pigmented nevus; machine learning; human; priority journal; article; deep learning; computer algorithm; international symposium on biomedical imaging; reader study; international skin imaging collaboration; automated melanoma diagnosis |
| Journal Title: | Journal of the American Academy of Dermatology |
| Volume: | 82 |
| Issue: | 3 |
| ISSN: | 0190-9622 |
| Publisher: | Mosby Elsevier |
| Date Published: | 2020-03-01 |
| Start Page: | 622 |
| End Page: | 627 |
| Language: | English |
| DOI: | 10.1016/j.jaad.2019.07.016 |
| PUBMED: | 31306724 |
| PROVIDER: | scopus |
| PMCID: | PMC7006718 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 March 2020 -- Source: Scopus |
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