The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network.
Open accessAbstract
Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.
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“Several algorithms incorporating artificial intelligence (AI) have been developed to help interpret both clinical11,12,13,14,15 and dermoscopic16,17,18,19,20,21,22,23 images for a variety of skin conditions”
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- He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. · DOI · Paper page
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Metadata
- Authors
- Han, S. S., Park, G. H., Lim, W., Kim, M. S., Na, J. I., Park, I., & Chang, S. E.
- Affiliations
- Hallym University (KR)
- Inje University (KR)
- Seoul National University (KR)
- Chonnam National University (KR)
- Asan Medical Center (KR)
- Corresponding author
- Chang, Sung Eun
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-01-19
- Volume
- 13
- Issue
- 1
- Pages
- e0191493
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5774804
- OpenAlex
- W2785200097
- Cited by
- 305
- References
- 18
- Citations by year
- 2026: 24 · 2025: 32 · 2024: 40 · 2023: 33 · 2022: 58 · 2021: 37 · 2020: 49 · 2019: 24 · 2018: 7
- FWCI
- 22.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Dermatology › Nail Diseases and Treatments
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- onychomycosis, automated diagnosis, dermatologist comparison, R-CNN, ResNet-152, VGG19, ensemble learning, dermatological image analysis
- MeSH terms
- Machine Learning, Dermatologists, Adult, Aged, Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Female, Foot Dermatoses, Hand Dermatoses, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Onychomycosis, Databases, Factual, Neural Networks, Computer, Area Under Curve, Young Adult
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