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

Ning Hung,Wei-Chi Wu,Eugene Yu-Chuan Kang,Andy Kuan-Yu Shih,Chang-Fu Kuo,Chihung Lin,Yih-Shiou Hwang,Ming-Tse Kuo,Ching-Hsi Hsiao

doi:10.3390/diagnostics11071246

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 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.

Highlights

Microbial keratitis (MK) is a serious corneal disease that can lead to reduced vision and even blindness [1,2]
Previous studies have reported that with an image-only diagnosis, general ophthalmologists are only able to correctly distinguish fungal keratitis (FK) from bacterial keratitis (BK) 49.3–67.1% of the time [9,10], and this percentage ranges from 66.0% to 75.9% among corneal specialists [10,11]
We aimed to develop a deep learning model that uses cropped slit-lamp images and to improve the prediction in differentiating BK and FK

Summary

Introduction

Microbial keratitis (MK) is a serious corneal disease that can lead to reduced vision and even blindness [1,2]. The annual incidence of MK as a cause of monocular blindness ranges from 1.5 to 2 million cases worldwide [3]. It is considered an epidemic, within South Asia, Southeast Asia, and East Asia, and in regions where fungal keratitis (FK) accounts for more than 50% of all MK cases [4]. Previous studies have reported that with an image-only diagnosis, general ophthalmologists are only able to correctly distinguish FK from bacterial keratitis (BK) 49.3–67.1% of the time [9,10], and this percentage ranges from 66.0% to 75.9% among corneal specialists [10,11].

Identification of Microbial Keratitis

Exclusion Criteria

Image Collection

Algorithm Architecture

Performance of Different Models

Conclusions