Supplementary Material for: Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks Article Swipe

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Brigid Betz‐Stablein , B D'Alessandro , Koh U. , Elsemieke I. Plasmeijer , Monika Janda , M Sadewa Wicaksana W , Rainer Hofmann‐Wellenhof , Green A.C. , Soyer H.P. · YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.6084/m9.figshare.14931900

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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dataset

Language

en

Landing Page

https://doi.org/10.6084/m9.figshare.14931900

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gold

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10

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https://openalex.org/W4394107014

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OpenAlex ID

https://openalex.org/W4394107014

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DOI

https://doi.org/10.6084/m9.figshare.14931900

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Title

Supplementary Material for: Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks

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dataset

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Language

en

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Publication year

2021

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Publication date

2021-01-01

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Brigid Betz‐Stablein, B D'Alessandro, Koh U., Elsemieke I. Plasmeijer, Monika Janda, M Sadewa Wicaksana W, Rainer Hofmann‐Wellenhof, Green A.C., Soyer H.P.

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https://doi.org/10.6084/m9.figshare.14931900

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Yes

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gold

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https://doi.org/10.6084/m9.figshare.14931900

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Concepts

Convolutional neural network, Photography, Cartography, Pattern recognition (psychology), Artificial intelligence, Computer science, Computer graphics (images), Geography, Art, Visual arts

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0

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10

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