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

PLoS One. 2018 Jan 19;13(1):e0191493. doi: 10.1371/journal.pone.0191493. eCollection 2018.


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.

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • Area Under Curve
  • Artificial Intelligence
  • Databases, Factual
  • Dermatologists
  • Diagnosis, Computer-Assisted*
  • Female
  • Foot Dermatoses / diagnosis
  • Foot Dermatoses / pathology
  • Hand Dermatoses / diagnosis
  • Hand Dermatoses / pathology
  • Humans
  • Image Interpretation, Computer-Assisted
  • Machine Learning
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Onychomycosis / diagnosis*
  • Onychomycosis / pathology
  • Young Adult

Grants and funding

The authors received no specific funding for this work. SK Telecom provided support in the form of salaries for author WL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.