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

• 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 in-dependent dermatologists. The secondary aim was to address which clinical and dermoscopic features derma-tologists 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 Sahl-grenska 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 der-matologists ranged from 0.75 (95% confidence in-terval 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 interob-server agreement, and these features also correlated with invasive melanoma. Prospective trials are needed to address the clinical usefulness of CNN models in this setting.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Dermatologi och venereologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Dermatology and Venereal Diseases (hsv//eng)

Nyckelord

artificial intelligence

clinical decision-making

me-lanoma

neural

network

computer

supervised machine learning

Publikations- och innehållstyp

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