Does sex matter?

• Verfasst von: Kommoss, Katharina [VerfasserIn]
• Verfasst von: Winkler, Julia K. [VerfasserIn]
• Verfasst von: Müller-Christmann, Christine [VerfasserIn]
• Verfasst von: Niedermair, Felicitas [VerfasserIn]
• Verfasst von: Toberer, Ferdinand [VerfasserIn]
• Verfasst von: Buhl, Timo [VerfasserIn]
• Verfasst von: Enk, Alexander [VerfasserIn]
• Verfasst von: Blum, Andreas [VerfasserIn]
• Verfasst von: Stolz, Wilhelm [VerfasserIn]
• Verfasst von: Rosenberger, Albert [VerfasserIn]
• Verfasst von: Hänßle, Holger [VerfasserIn]
• Titel: Does sex matter?
• Titelzusatz: Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection
• Verf.angabe: Katharina Sies, Julia K. Winkler, Christine Fink, Felicitas Bardehle, Ferdinand Toberer, Timo Buhl, Alexander Enk, Andreas Blum, Wilhelm Stolz, Albert Rosenberger, Holger A. Haenssle
• E-Jahr: 2022
• Jahr: 16 February 2022
• Umfang: 7 S.
• Fussnoten: Gesehen am 24.06.2022
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2022
• Band/Heft Quelle: 164(2022) vom: März, Seite 88-94
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2021.12.034
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Automated melanoma detection
• Sach-SW: Bias
• Sach-SW: Computer-assisted diagnostics
• Sach-SW: Convolutional neural network
• Sach-SW: Deep learning
• Sach-SW: Dermoscopy
• Sach-SW: Melanoma
• Sach-SW: Nevi
• Sach-SW: Pigmented skin lesions
• Sach-SW: Sex
• K10plus-PPN: 1807742938

Abstract

Background - Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany). - Methods - We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776). - Results - Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59). - Conclusion - Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions.