1. Skin lesions of face and scalp – Classification by a market-approved convolutional neural network in comparison with 64 dermatologists
- Author
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Holger Andreas Haenssle, Julia Katharina Winkler, Christine Fink, Ferdinand Toberer, Alexander Enk, Wilhelm Stolz, Teresa Deinlein, Rainer Hofmann-Wellenhof, Harald Kittler, Philipp Tschandl, Cliff Rosendahl, Aimilios Lallas, Andreas Blum, Mohamed Souhayel Abassi, Luc Thomas, Isabelle Tromme, Albert Rosenberger, Marie Bachelerie, Sonali Bajaj, Alise Balcere, Sophie Baricault, Clément Barthaux, Yvonne Beckenbauer, Ines Bertlich, Marie-France Bouthenet, Sophie Brassat, Philipp Marcel Buck, Kristina Buder-Bakhaya, Maria-Letizia Cappelletti, Cécile Chabbert, Julie De Labarthe, Eveline DeCoster, Michèle Dobler, Daphnée Dumon, Steffen Emmert, Julie Gachon-Buffet, Mikhail Gusarov, Franziska Hartmann, Julia Hartmann, Anke Herrmann, Isabelle Hoorens, Eva Hulstaert, Raimonds Karls, Andreea Kolonte, Christian Kromer, Céline Le Blanc Vasseux, Annabelle Levy-Roy, Pawel Majenka, Marine Marc, Veronique Martin Bourret, Nadège Michelet-Brunacci, Christina Mitteldorf, Jean Paroissien, Camille Picard, Diana Plise, Valérie Reymann, Fabrice Ribeaudeau, Pauline Richez, Hélène Roche Plaine, Deborah Salik, Elke Sattler, Sarah Schäfer, Roland Schneiderbauer, Thierry Secchi, Karen Talour, Lukas Trennheuser, Alexander Wald, Priscila Wölbing, and Pascale Zukervar
- Subjects
Adult ,Male ,0301 basic medicine ,Cancer Research ,medicine.medical_specialty ,Adolescent ,Early detection ,Dermoscopy ,Lentigo maligna ,Skin Diseases ,Convolutional neural network ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Image Processing, Computer-Assisted ,Humans ,Medicine ,Child ,Aged ,Aged, 80 and over ,Scalp ,business.industry ,Actinic keratosis ,Middle Aged ,Prognosis ,medicine.disease ,Dermatology ,Textual information ,030104 developmental biology ,medicine.anatomical_structure ,Oncology ,Child, Preschool ,Face ,030220 oncology & carcinogenesis ,Female ,Level ii ,business ,Skin lesion ,Dermatologists ,Follow-Up Studies - Abstract
The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking.A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL. The same lesions were used in a two-level reader study including 64 dermatologists (level I: dermoscopy only; level II: dermoscopy, clinical close-up images, textual information). Primary endpoints were the CNN's sensitivity and specificity in comparison with the dermatologists' management decisions in level II. Generalizability of the CNN results was tested by using four additional external data sets.The CNN's sensitivity, specificity and ROC AUC were 96.2% [87.0%-98.9%], 68.8% [54.7%-80.1%] and 0.929 [0.880-0.978], respectively. In level II, the dermatologists' management decisions showed a mean sensitivity of 84.2% [82.2%-86.2%] and specificity of 69.4% [66.0%-72.8%]. When fixing the CNN's specificity at the dermatologists' mean specificity (69.4%), the CNN's sensitivity (96.2% [87.0%-98.9%]) was significantly higher than that of dermatologists (84.2% [82.2%-86.2%]; p 0.001). Dermatologists of all training levels were outperformed by the CNN (all p 0.001). In confirmation, the CNN's accuracy (83.0%) was significantly higher than dermatologists' accuracies in level II management decisions (all p 0.001). The CNN's performance was largely confirmed in three additional external data sets but particularly showed a reduced specificity in one Australian data set including FSL on severely sun-damaged skin.When applied as an assistant system, the CNN's higher sensitivity at an equivalent specificity may result in an improved early detection of face and scalp skin cancers.
- Published
- 2021
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