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Article

Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks

1
Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan
2
College of Medicine, Chang Gung University, No. 261, Wenhua 1st Rd., Kweishan, Taoyuan 333, Taiwan
3
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5 Fu-Hsin Rd, Kweishan, Taoyuan 333, Taiwan
4
Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital, No. 123, Dapi Rd, Niaosong, Kaohsiung 833, Taiwan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Jae-Ho Han
Diagnostics 2021, 11(7), 1246; https://doi.org/10.3390/diagnostics11071246
Received: 12 May 2021 / Revised: 9 July 2021 / Accepted: 10 July 2021 / Published: 12 July 2021
(This article belongs to the Special Issue Artificial Intelligence in Eye Disease)
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 1 January 2010 and 31 December 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 different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. 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 heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists. View Full-Text
Keywords: deep learning; infectious keratitis; cropped corneal image; slit-lamp images deep learning; infectious keratitis; cropped corneal image; slit-lamp images
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MDPI and ACS Style

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. https://doi.org/10.3390/diagnostics11071246

AMA Style

Hung N, Shih AK-Y, Lin C, Kuo M-T, Hwang Y-S, Wu W-C, Kuo C-F, Kang EY-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(7):1246. https://doi.org/10.3390/diagnostics11071246

Chicago/Turabian Style

Hung, Ning, Andy K.-Y. Shih, Chihung Lin, Ming-Tse Kuo, Yih-Shiou Hwang, Wei-Chi Wu, Chang-Fu Kuo, Eugene Y.-C. Kang, and Ching-Hsi Hsiao. 2021. "Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks" Diagnostics 11, no. 7: 1246. https://doi.org/10.3390/diagnostics11071246

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