Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

PURPOSE

We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) vs glaucomatous optic neuropathy (GON) on color fundus photographs by the use of image recognition.

DESIGN

Development of an Artificial Intelligence Classification algorithm.

METHODS

This single-institution analysis included 3815 fundus images from the picture archiving and communication system of Seoul National University Bundang Hospital consisting of 2883 normal optic disc images, 446 NGON images, and 486 GON images. The presence of NGON and GON was interpreted by 2 expert neuro-ophthalmologists and had corroborated evidence on visual field testing and optical coherence tomography. Images were preprocessed in size and color enhancement before input. We applied the convolutional neural network (CNN) of ResNet-50 architecture. The area under the precision-recall curve (average precision) was evaluated for the efficacy of deep learning algorithms to assess the performance of classifying NGON and GON.

RESULTS

The diagnostic accuracy of the ResNet-50 model to detect GON among NGON images showed a sensitivity of 93.4% and specificity of 81.8%. The area under the precision-recall curve for differentiating NGON vs GON showed an average precision value of 0.874. False positive cases were found with extensive areas of peripapillary atrophy and tilted optic discs.

CONCLUSION

Artificial intelligence-based deep learning algorithms for detecting optic disc diseases showed excellent performance in differentiating NGON and GON on color fundus photographs, necessitating further research for clinical application.