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

International Skin Imaging Collaboration

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)622-627
Number of pages6
JournalJournal of the American Academy of Dermatology
Volume82
Issue number3
DOIs
StatePublished - Mar 2020

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

ASJC Scopus subject areas

  • Dermatology

Fingerprint Dive into the research topics of 'Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017'. Together they form a unique fingerprint.

  • Cite this