Hung, N.; Shih, A.K.-Y.; Lin, C.; Kuo, M.-T.; Hwang, Y.-S.; Wu, W.-C.; Kuo, C.-F.; Kang, E.Y.-C.; Hsiao, C.-H. Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks. Diagnostics2021, 11, 1246.
Hung, N.; Shih, A.K.-Y.; Lin, C.; Kuo, M.-T.; Hwang, Y.-S.; Wu, W.-C.; Kuo, C.-F.; Kang, E.Y.-C.; Hsiao, C.-H. Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks. Diagnostics 2021, 11, 1246.
Hung, N.; Shih, A.K.-Y.; Lin, C.; Kuo, M.-T.; Hwang, Y.-S.; Wu, W.-C.; Kuo, C.-F.; Kang, E.Y.-C.; Hsiao, C.-H. Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks. Diagnostics2021, 11, 1246.
Hung, N.; Shih, A.K.-Y.; Lin, C.; Kuo, M.-T.; Hwang, Y.-S.; Wu, W.-C.; Kuo, C.-F.; Kang, E.Y.-C.; Hsiao, C.-H. Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks. Diagnostics 2021, 11, 1246.
Abstract
In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between January 1, 2010, and December 31, 2019, from two medical centers in Taiwan. We constructed a deep learning algorithm, consisting of a segmentation model for cropping cornea images and a classification model that applies convolutional neural networks to differentiate between FK and BK. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heatmap of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved an average diagnostic accuracy of 80.00%. The diagnostic accuracy for BK ranged from 79.59% to 95.91% and that for FK ranged from 26.31% to 63.15%. DenseNet169 showed the best model performance, with an AUC of 0.78 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.
Keywords
Deep learning, infectious keratitis, cropped corneal image, slit-lamp images
Subject
Medicine and Pharmacology, Immunology and Allergy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
*
