C Fink1, A Blum2, T Buhl3, C Mitteldorf3, R Hofmann-Wellenhof4, T Deinlein4, W Stolz5, L Trennheuser1, C Cussigh1, D Deltgen1, J K Winkler1, F Toberer1, A Enk1, A Rosenberger6, H A Haenssle1. 1. Department of Dermatology, University of Heidelberg, Heidelberg, Germany. 2. Public, Private and Teaching Practice, Konstanz, Germany. 3. Department of Dermatology and Allergology, University Medical Center Göttingen, Göttingen, Germany. 4. Department of Dermatology and Venerology, Medical University of Graz, Graz, Austria. 5. Department of Dermatology, Allergology and Environmental Medicine II, Hospital Thalkirchner Street, Munich, Germany. 6. Department of Genetic Epidemiology, University Medical Center of Goettingen, Goettingen, Germany.
Abstract
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. OBJECTIVE: To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. METHODS: In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3 mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. RESULTS: The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P = 0.092), 71.0% (95% CI [62.6-78.1]; P = 0.256) and 24 (95% CI [11.6-48.4]; P = 0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR = 12) but best with CNN verification (DOR = 98). CONCLUSION: The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.
BACKGROUND:Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated. OBJECTIVE: To assess the diagnostic performance of a CNN when used to differentiate melanomas from combined naevi in comparison with dermatologists. METHODS: In this study, a CNN with regulatory approval for the European market (Moleanalyzer-Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was used. We attained a dichotomous classification (benign, malignant) in dermoscopic images of 36 combined naevi and 36 melanomas with a mean Breslow thickness of 1.3 mm. Primary outcome measures were the CNN's sensitivity, specificity and the diagnostic odds ratio (DOR) in comparison with 11 dermatologists with different levels of experience. RESULTS: The CNN revealed a sensitivity, specificity and DOR of 97.1% (95% CI [82.7-99.6]), 78.8% (95% CI [62.8-89.1.3]) and 34 (95% CI [4.8-239]), respectively. Dermatologists showed a lower mean sensitivity, specificity and DOR of 90.6% (95% CI [84.1-94.7]; P = 0.092), 71.0% (95% CI [62.6-78.1]; P = 0.256) and 24 (95% CI [11.6-48.4]; P = 0.1114). Under the assumption that dermatologists use the CNN to verify their (initial) melanoma diagnosis, dermatologists achieve an increased specificity of 90.3% (95% CI [79.8-95.6]) at an almost unchanged sensitivity. The largest benefit was observed in 'beginners', who performed worst without CNN verification (DOR = 12) but best with CNN verification (DOR = 98). CONCLUSION: The tested CNN more accurately classified combined naevi and melanomas in comparison with trained dermatologists. Their diagnostic performance could be improved if the CNN was used to confirm/overrule an initial melanoma diagnosis. Application of a CNN may therefore be of benefit to clinicians.
Authors: Claire M Felmingham; Nikki R Adler; Zongyuan Ge; Rachael L Morton; Monika Janda; Victoria J Mar Journal: Am J Clin Dermatol Date: 2021-03 Impact factor: 7.403
Authors: Jana Sami; Sophie Lemoupa Makajio; Emilien Jeannot; Bruno Kenfack; Roser Viñals; Pierre Vassilakos; Patrick Petignat Journal: Healthcare (Basel) Date: 2022-02-18