BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuable tool for image classification in dermatology. OBJECTIVES: To test whether automated, reproducible naevus counts are possible through the combination of convolutional neural networks (CNN) and three-dimensional (3D) total body imaging. METHODS: Total body images from a study of naevi in the general population were used for the training (82 subjects, 57,742 lesions) and testing (10 subjects; 4,868 lesions) datasets for the development of a CNN. Lesions were labelled as naevi, or not ("non-naevi"), by a senior dermatologist as the gold standard. Performance of the CNN was assessed using sensitivity, specificity, and Cohen's kappa, and evaluated at the lesion level and person level. RESULTS: Lesion-level analysis comparing the automated counts to the gold standard showed a sensitivity and specificity of 79% (76-83%) and 91% (90-92%), respectively, for lesions =2 mm, and 84% (75-91%) and 91% (88-94%) for lesions =5 mm. Cohen's kappa was 0.56 (0.53-0.59) indicating moderate agreement for naevi =2 mm, and substantial agreement (0.72, 0.63-0.80) for naevi =5 mm. For the 10 individuals in the test set, person-level agreement was assessed as categories with 70% agreement between the automated and gold standard counts. Agreement was lower in subjects with numerous seborrhoeic keratoses. CONCLUSION: Automated naevus counts with reasonable agreement to those of an expert clinician are possible through the combination of 3D total body photography and CNNs. Such an algorithm may provide a faster, reproducible method over the traditional in person total body naevus counts.
| Authors | Betz-Stablein, Brigid; D'Alessandro, Brian; Koh, Uyen; Plasmeijer, Elsemieke; Janda, Monika; Menzies, Scott W; Hofmann-Wellenhof, Rainer; Green, Adele C; Soyer, H Peter |
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| Journal | Dermatology (Basel, Switzerland) |
| Pages | 1-8 |
| Volume | 238 |
| Date | 1/01/2021 |
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| URL | http://www.ncbi.nlm.nih.gov/pubmed/?term=10.1159/000517218 |