Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks

Brigid Betz-Stablein, Brian D'Alessandro, Uyen Koh, Elsemieke Plasmeijer, Monika Janda, Scott W Menzies, Rainer Hofmann-Wellenhof, Adele C Green, H Peter Soyer

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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.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalDermatology
Early online date8 Jul 2021
DOIs
Publication statusPublished - 8 Jul 2021

Keywords

  • 3D total body imaging
  • Artificial intelligence
  • Melanocytic naevi
  • Melanoma
  • Moles

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