Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis

Abstract

Purpose: Microbial keratitis is an urgent condition in ophthalmology that requires prompt treatment. This study aimed to apply deep learning algorithms for rapidly discriminating between fungal keratitis (FK) and bacterial keratitis (BK).

Methods: A total of 2167 anterior segment images retrospectively acquired from 194 patients with 128 patients with BK (1388 images, 64.1%) and 66 patients with FK (779 images, 35.9%) were used to develop the model. The images were split into training, validation, and test sets. Three convolutional neural networks consisting of VGG19, ResNet50, and DenseNet121 were trained to classify images. Performance of each model was evaluated using precision (positive predictive value), sensitivity (recall), F1 score (test's accuracy), and area under the precision-recall curve (AUPRC). Ensemble learning was then applied to improve classification performance.

Results: The classification performance in F1 score (95% confident interval) of VGG19, DenseNet121, and RestNet50 was 0.78 (0.72-0.84), 0.71 (0.64-0.78), and 0.68 (0.61-0.75), respectively. VGG19 also demonstrated the highest AUPRC of 0.86 followed by RestNet50 (0.73) and DenseNet (0.60). The ensemble learning could improve performance with the sensitivity and F1 score of 0.77 (0.81-0.83) and 0.83 (0.77-0.89) with an AUPRC of 0.904.

Conclusions: Convolutional neural network with ensemble learning showed the best performance in discriminating FK from BK compared with single architecture models. Our model can potentially be considered as an adjunctive tool for providing rapid provisional diagnosis in patients with microbial keratitis.

FIGURE 1.

Distribution of BK and FK in each data set.

FIGURE 2.

Precision–recall curves in the test data set obtained by 4 different models.

FIGURE 3.

Misclassification analysis using Grad-CAM in patients with FK. The optimal decision threshold of FK in VGG19 was 0.39. The probability of FK [P(FK)] is presented at the top of each image. The overlay heatmap with Grad-CAM analysis highlighted the areas that influenced model prediction. Image (E) was misclassified as BK with the P(FK) of 0.21; this was lower than the decision threshold.

FIGURE 4.

Variation of prediction probability on brightness adjustment in a misclassified image. A, Images with brightness adjustment by adding a constant of −200, −100, 0, 100, and 200 to all pixels and (B) graph demonstrates prediction probability (y axis) at different pixel value adjustments (x axis). C, Box plot illustrates an optimal brightness range of image for achieving correct classification which was 24.70 (range 9.63–45.20). The lower and higher image brightness can lead to image misclassification.