Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Abstract

Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis.

Keywords: automatic classification; deep learning; infectious keratitis; slit lamp image.

Figure 1

Representative slit lamp images of the normal cornea (Normal), viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK), from top to bottom. Each image was from a different eye, and each category of keratitis showed a different degree of infection.

Figure 2

(A) The flowchart of the DL diagnostic system for IK based on slit lamp images. Firstly, the input images were preprocessed and divided into three data sets. Next, data augmentation and data processing were performed on the images. Then, various DL algorithms were used for model development, optimization, and evaluation. Finally, the results of the models were output in the form of heatmaps and class labels. (B) The architecture of EfficientNetV2. It was mainly composed of several Fused-MBConv blocks and MBConv blocks. Early features were extracted using Fused-MBConv blocks, which used 1 × 1 Conv, 3 × 3 Conv and SE layers. It used depthwise convolution to extract from MBConv blocks. Compared with the traditional convolution widely used in other models, depthwise convolution had fewer parameters and computation.

Figure 2

(A) The flowchart of the DL diagnostic system for IK based on slit lamp images. Firstly, the input images were preprocessed and divided into three data sets. Next, data augmentation and data processing were performed on the images. Then, various DL algorithms were used for model development, optimization, and evaluation. Finally, the results of the models were output in the form of heatmaps and class labels. (B) The architecture of EfficientNetV2. It was mainly composed of several Fused-MBConv blocks and MBConv blocks. Early features were extracted using Fused-MBConv blocks, which used 1 × 1 Conv, 3 × 3 Conv and SE layers. It used depthwise convolution to extract from MBConv blocks. Compared with the traditional convolution widely used in other models, depthwise convolution had fewer parameters and computation.

Figure 3

The macro-average ROC curves and AUCs of six DL models. The AUCs of the models ranged from 0.81–0.86.

Figure 4

The macro-average ROC curves and AUCs of the EffecientNetV2-M model and two ophthalmologists (A). The ROC curves for each category of the EffecientNetV2-M model (B), Doctor 1 (C) and Doctor 2 (D).

Figure 5

The confusion matrix of the EffecientNetV2-M model (A) and the average results of two ophthalmologists (B) in the test set. The column denotes the predicted labels, and the row indicates the true labels.

Figure 6

The heatmaps generated by the EffecientNetV2-M model. From top to bottom, each row corresponds to the images of viral keratitis (VK), fungal keratitis (FK) and bacterial keratitis (BK). Columns (A,C) are different original image examples of images of each category, while column (B) are the heatmaps generated by column (A), and column (D) are the heatmaps generated by column (C).