Cited by 310Nail Diseases and TreatmentsAdvanced Neural Network ApplicationsArtificial Intelligence in Healthcare and Education

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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.

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Han, Seung Seog, Park, Gyeong Hun, Lim, Woohyung, Kim, Myoung Shin, Na, Jung Im, Park, Ilwoo, Chang, Sung Eun·2018·PLoS ONE

DOI 10.1371/journal.pone.0191493PMC PMC5774804

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Abstract

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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Abbreviations
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.002.0006
HypeDx
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0006
How to Make It as an AI
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0004
Introduction
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0001
Ethics, Justice, and Health AI
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0007
Copyright Page
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.002.0003
Technical Appendix
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.005.0001
Conclusion
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0009
Regulating Health AI
Graham, S. S. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780197644461.003.0008
Notes
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Additional file 2 of Transfer learning for medical image classification: a literature review
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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: Han, Seung Seog
Park, Gyeong Hun
Lim, Woohyung
Kim, Myoung Shin
Na, Jung Im
Park, Ilwoo
Chang, Sung Eun
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2018-01-19
Volume: 13
Issue: 1
Pages: e0191493
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0191493
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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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.

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