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Reproducible naevus counts using 3D total body photography and convolutional neural networks
Betz-Stablein, B. D'Alessandro, B. Koh, U. Plasmeijer, E. Janda, M. Menzies, S. W. Hofmann-Wellenhof, R. Green, Adèle C Soyer, H. P.
Betz-Stablein, B.
D'Alessandro, B.
Koh, U.
Plasmeijer, E.
Janda, M.
Menzies, S. W.
Hofmann-Wellenhof, R.
Green, Adèle C
Soyer, H. P.
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Abstract
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.
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Date
2021
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Betz-Stablein B, D’Alessandro B, Koh U, Plasmeijer E, Janda M, Menzies SW, et al. Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks. Dermatology. 2021 Jul 8;1–8.