A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

Journal: Journal of dermatological science
Published Date: Dec 2, 2020

Abstract

BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate accuracy and reproducibility in discriminating atypical nevi (AN) from early melanomas (EM).

Authors

  • Linda Tognetti
    Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy. Electronic address: linda.tognetti@dbm.unisi.it.
  • Simone Bonechi
    Department of Information Engineering and Mathematics, University of Siena, Siena, Italy; Department of Economy Engineering Society and Buisiness, Tuscia University, Viterbo, Italy.
  • Paolo Andreini
    Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
  • Monica Bianchini
    Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
  • Franco Scarselli
    Department of Information Engineering and Mathematics, University of Siena, Italy. Electronic address: franco@diism.unisi.it.
  • Gabriele Cevenini
    Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy.
  • Elvira Moscarella
    Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy.
  • Francesca Farnetani
    Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy.
  • Caterina Longo
    Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy; Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Centro Oncologico ad Alta Tecnologia Diagnostica-Dermatologia, Reggio Emilia, Italy.
  • Aimilios Lallas
  • Cristina Carrera
    Hospital Clínic de Barcelona, Universitat de Barcelona, Department of Dermatology, Barcelona, Spain.
  • Susana Puig
    Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Rarasd (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain.
  • Danica Tiodorovic
    Dermatology Clinic, Medical Faculty, Nis University, Nis, Serbia.
  • Jean Luc Perrot
    Dermatology Unit, University Hospital of St-Etienne, Saint Etienne, France.
  • Giovanni Pellacani
    Facolta di Medicina et Chirugia, UNIMORE Iniversita Degli Studi di Modena e Reggio Emilia, Modena, Italy.
  • Giuseppe Argenziano
    Dermatology Unit, University of Campania, Naples, Italy.
  • Elisa Cinotti
    Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy.
  • Gennaro Cataldo
    Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy.
  • Alberto Balistreri
    Bioengineering Unit, Department of Medical Biotechnology, University of Siena, Italy.
  • Alessandro Mecocci
    Department of Information Engineering and Mathematics, University of Siena, Siena, Italy.
  • Marco Gori
    Department of Information Engineering and Mathematics, University of Siena, Italy. Electronic address: marco.gori@unisi.it.
  • Pietro Rubegni
    Dermatology Unit, Department of Medical, Surgical and Neurosciences, University of Siena, Italy.
  • Alessandra Cartocci
    Department of Medical Biotechnologies, University of Siena, Siena, Italy.

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