Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

Journal: European journal of cancer (Oxford, England : 1990)
Published Date: Aug 10, 2025

Abstract

IMPORTANCE: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM. We hypothesized that support from DL-CNN could increase dermatologists' accuracy in differentiating CM from MelSim.

Authors

  • A S Vollmer
    Department of Dermatology, University Medical Center Heidelberg, Germany. Electronic address: anastasia.vollmer@med.uni-heidelberg.de.
  • J K Winkler
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • K S Kommoss
    Department of Dermatology, University Medical Center Heidelberg, Germany.
  • A Blum
    Office Based Clinic of Dermatology, Konstanz, Germany.
  • W Stolz
    Department of Dermatology, Allergology and Environmental Medicine II, Hospital Thalkirchner Street, Munich, Germany.
  • A Enk
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • H A Haenssle
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany. Electronic address: Holger.Haenssle@med.uni-heidelberg.de.

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