Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

Journal: The Lancet. Oncology
PMID: 31201137

Abstract

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions.

Authors

  • Philipp Tschandl
    Department of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.
  • Noel Codella
    IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
  • Bengu Nisa Akay
    Department of Dermatology, Ankara University Faculty of Medicine, Ankara, Turkey.
  • Giuseppe Argenziano
    Dermatology Unit, University of Campania, Naples, Italy.
  • Ralph P Braun
    Department of Dermatology, University Hospital Zürich, Zürich, Switzerland.
  • Horacio Cabo
  • David Gutman
    Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
  • Allan Halpern
    Dermatology Service, Memorial Sloan-Kettering Cancer Center, New York, NY.
  • Brian Helba
    Kitware, Clifton Park, NY, USA.
  • Rainer Hofmann-Wellenhof
    Department of Dermatology, Medical University Graz, Graz, Austria.
  • Aimilios Lallas
  • Jan Lapins
    Department of Dermatology, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden.
  • 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.
  • Josep Malvehy
    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.
  • Michael A Marchetti
    Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA. Electronic address: marchetm@mskcc.org.
  • Ashfaq Marghoob
    Dermatology Service, Memorial Sloan Kettering Cancer Center, Hauppauge, New York.
  • Scott Menzies
    Sydney Melanoma Diagnostic Centre and Discipline of Dermatology, University of Sydney, Sydney, Australia.
  • Amanda Oakley
    Department of Dermatology, Waikato District Health Board and Waikato Clinical Campus, University of Auckland, Hamilton, New Zealand.
  • John Paoli
  • 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.
  • Christoph Rinner
    Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
  • Cliff Rosendahl
    School of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
  • Alon Scope
    Medical Screening Institute, Chaim Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Christoph Sinz
    ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria. Electronic address: christoph.sinz@meduniwien.at.at.
  • H Peter Soyer
    Dermatology Research Centre, The University of Queensland, The University of Queensland Diamantina Institute, Brisbane, Australia.
  • Luc Thomas
    Department of Dermatology, Centre Hospitalier Lyon Sud, Lyon 1 University, Lyons Cancer Research Center, Lyon, France.
  • Iris Zalaudek
  • Harald Kittler
    ViDIR Group, Department of Dermatology, Medical University of Vienna, Vienna, Austria. Electronic address: harald.kittler@meduniwien.at.at.

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