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Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
Open accessAbstract
IMPORTANCE: Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in the diagnosis of melanoma. Accordingly, further exploring the potential limitations of CNN technology before broadly applying it is of special interest. OBJECTIVE: To investigate the association between gentian violet surgical skin markings in dermoscopic images and the diagnostic performance of a CNN approved for use as a medical device in the European market. DESIGN AND SETTING: A cross-sectional analysis was conducted from August 1, 2018, to November 30, 2018, using a CNN architecture trained with more than 120 000 dermoscopic images of skin neoplasms and corresponding diagnoses. The association of gentian violet skin markings in dermoscopic images with the performance of the CNN was investigated in 3 image sets of 130 melanocytic lesions each (107 benign nevi, 23 melanomas). EXPOSURES: The same lesions were sequentially imaged with and without the application of a gentian violet surgical skin marker and then evaluated by the CNN for their probability of being a melanoma. In addition, the markings were removed by manually cropping the dermoscopic images to focus on the melanocytic lesion. MAIN OUTCOMES AND MEASURES: Sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the CNN's diagnostic classification in unmarked, marked, and cropped images. RESULTS: In all, 130 melanocytic lesions (107 benign nevi and 23 melanomas) were imaged. In unmarked lesions, the CNN achieved a sensitivity of 95.7% (95% CI, 79%-99.2%) and a specificity of 84.1% (95% CI, 76.0%-89.8%). The ROC AUC was 0.969. In marked lesions, an increase in melanoma probability scores was observed that resulted in a sensitivity of 100% (95% CI, 85.7%-100%) and a significantly reduced specificity of 45.8% (95% CI, 36.7%-55.2%, P < .001). The ROC AUC was 0.922. Cropping images led to the highest sensitivity of 100% (95% CI, 85.7%-100%), specificity of 97.2% (95% CI, 92.1%-99.0%), and ROC AUC of 0.993. Heat maps created by vanilla gradient descent backpropagation indicated that the blue markings were associated with the increased false-positive rate. CONCLUSIONS AND RELEVANCE: This study's findings suggest that skin markings significantly interfered with the CNN's correct diagnosis of nevi by increasing the melanoma probability scores and consequently the false-positive rate. A predominance of skin markings in melanoma training images may have induced the CNN's association of markings with a melanoma diagnosis. Accordingly, these findings suggest that skin markings should be avoided in dermoscopic images intended for analysis by a CNN. TRIAL REGISTRATION: German Clinical Trial Register (DRKS) Identifier: DRKS00013570.
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“An example of this use was when explanatory heat maps revealed that an AI model trained to detect skin cancer was focusing more on the surgical skin markings present on the images rather than the skin lesions.42”
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“For example, an artificial intelligence system might learn to detect surgical skin markings to diagnose skin cancer, rather than looking for features related to the lesion itself.9”
From The medical algorithmic audit (2022) · cites this paper as [9]
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Metadata
- Authors
- Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., & Haenssle, H. A.
- Affiliations
- Heidelberg University (DE)
- Medical University of Graz (AT)
- Department of Dermatology and Venerology, Medical University of Graz, Graz, Austria
- Hospices Civils de Lyon (FR)
- Aristotle University of Thessaloniki (GR)
- Public, Private, and Teaching Practice, Konstanz, Germany
- München Klinik (DE)
- Corresponding author
- Haenssle, Holger A.
- ORCID
- Venue
- JAMA Dermatology
- Publisher
- American Medical Association (AMA)
- Published
- 2019-10-01
- Volume
- 155
- Issue
- 10
- Pages
- 1135
- ISSN
- 2168-6068 (ISSN-L)
- PMC
- PMC6694463
- OpenAlex
- W2969096242
- Semantic Scholar
- 68d1001637ed1c78fc1cc0b7d54fdc15c05eb004
- Cited by
- 365
- References
- 27
- Citations by year
- 2026: 36 · 2025: 53 · 2024: 55 · 2023: 58 · 2022: 44 · 2021: 64 · 2020: 48 · 2019: 7
- FWCI
- 17.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- JAMA Dermatology (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Keywords
- melanoma, dermoscopy, convolutional neural networks, crystal violet, image cropping, diagnostic accuracy, benign nevi, computer-aided diagnosis, false positives, dermatologist comparison, skin lesion classification, medical device validation, skin cancer screening, melanocytic lesions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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