Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network

Authors

  • Martin Gillstedt
  • Ludwig Mannius
  • John Paoli
  • Johan Dahlén Gyllencreutz
  • Julia Fougelberg
  • Eva Johansson Backman
  • Jenna Pakka
  • Oscar Zaar
  • Sam Polesie Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gröna stråket 16, SE-413 45 Gothenburg, Sweden

DOI:

https://doi.org/10.2340/actadv.v102.2681

Keywords:

artificial intelligence, clinical decision-making, melanoma, neural networks, computer, supervised machine learning

Abstract

Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 independent dermatologists. The secondary aim was to address which clinical and dermoscopic features dermatologists found to be suggestive of invasive and in situ melanomas, respectively. A retrospective investigation was conducted including 1,578 cases of paired images of invasive (n = 728, 46.1%) and in situ melanomas (n = 850, 53.9%). All images were obtained from the Department of Dermatology and Venereology at Sahlgrenska University Hospital and were randomized to a training set (n = 1,078), a validation set (n = 200) and a test set (n = 300). The area under the receiver operating characteristics curve (AUC) among the dermatologists ranged from 0.75 (95% confidence interval 0.70–0.81) to 0.80 (95% confidence interval 0.75–0.85). The combined dermatologists’ AUC was 0.80 (95% confidence interval 0.77–0.86), which was significantly higher than the CNN model (0.73, 95% confidence interval 0.67–0.78, p = 0.001). Three of the dermatologists significantly outperformed the CNN. Shiny white lines, atypical blue-white structures and polymorphous vessels displayed a moderate interobserver agreement, and these features also correlated with invasive melanoma. Prospective trials are needed to address the clinical usefulness of CNN models in this setting.

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Published

2022-10-11

How to Cite

Gillstedt, M., Mannius, L., Paoli, J., Dahlén Gyllencreutz, J. ., Fougelberg, J., Johansson Backman, E., … Polesie, S. (2022). Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network. Acta Dermato-Venereologica, 102, adv00790. https://doi.org/10.2340/actadv.v102.2681