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

Tschandl, Philipp; Codella, Noel; Akay, BENGÜ; Argenziano, Giuseppe; Braun, Ralph; Cabo, Horacio; Gutman, David; Halpern, Allan; Helba, Brian; Hofmann-Wellenhof, Rainer; Lallas, Aimilios; Lapins, Jan; Longo, Caterina; Malvehy, Josep; Marchetti, Michael; Marghoob, Ashfaq; Menzies, Scott; Oakley, Amanda; Paoli, John; Puig, Susana; Rinner, Christoph; Rosendahl, Cliff; Scope, Alon; Sinz, Christoph; Soyer, H.; Thomas, Luc; Zalaudek, Iris; Kittler, Harald

doi:10.1016/s1470-2045(19)30333-x

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

Tschandl P., Codella N., Akay B. N., Argenziano G., Braun R. P., Cabo H., ...Daha Fazla

LANCET ONCOLOGY, cilt.20, sa.7, ss.938-947, 2019 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 20 Sayı: 7
Basım Tarihi: 2019
Doi Numarası: 10.1016/s1470-2045(19)30333-x
Dergi Adı: LANCET ONCOLOGY
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.938-947
Ankara Üniversitesi Adresli: Evet

Özet

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.