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Deep neural networks show a superior performance to all 42 dermatologists in onychomycosis diagnosis

 

We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish a nail from a background. We used R-CNN to generate training datasets of 49,567 images, which were 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 results from the AI ensemble model (ResNet-152 + VGG-19 + feedforward neural networks) showed the 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, respectively.

With the combination of the B1 and C datasets, the AI Youden index was significantly higher than that of 42 dermatologists who performed the same assessment manually (p = 0.01). For the combinations of B1+C and B2+D dataset, almost none of the dermatologists performed as well as the AI.


The deep learning model trained with the dataset comprising 49,567 images, achieved a diagnostic accuracy for the classification of onychomycosis, which was superior to that of most of the dermatologists who participated in this study.

 

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