Combining artificial intelligence and human expertise for more accurate dermoscopic melanoma diagnosis: A 2-session retrospective reader study - 14/05/24
Doi : 10.1016/j.jaad.2023.12.072
Mario Giulini, MD a, Mohamad Goldust, MD b, Stephan Grabbe, MD a, ⁎ 
, Christian Ludwigs, MSc c, Dominik Seliger, MSc c, Priyanka Karagaiah, MD d, Hadrian Schepler, MD a, Florian Butsch, MD a, Beate Weidenthaler-Barth, MD a, Stephan Rietz, MD a
, Christian Ludwigs, MSc c, Dominik Seliger, MSc c, Priyanka Karagaiah, MD d, Hadrian Schepler, MD a, Florian Butsch, MD a, Beate Weidenthaler-Barth, MD a, Stephan Rietz, MD a a Department of Dermatology, University Medical Center Mainz, Mainz, Germany
b Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut
c Aigora GmbH, Munich, Germany
d Department of Dermatology, Bangalore Medical College and Research Institute, Bengaluru, India
∗Correspondence to: Stephan Grabbe, MD, Department of Dermatology, University Medical Center Mainz, Langenbeckstrasse 1, 55131 Mainz, GermanyDepartment of DermatologyUniversity Medical Center MainzLangenbeckstrasse 1Mainz55131Germany
Key words : artificial intelligence, convolutional neural networks, dermoscopic diagnostics, melanoma, nevi, skin cancer
| Funding sources: None. |
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| Patient consent: Not applicable. |
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| IRB approval status: Reviewed and approved by the local ethics committee of Rhineland-Palatinate, Germany (approval number: 2019-14408_1-retrospektiv). |
© 2024
Pubblicato da Elsevier Masson SAS.
Vol 90 - N° 6
P. 1266-1268 - giugno 2024 Ritorno al numero
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