Deep learning (DL) has been used in differentiating a range of ophthalmic conditions. We describe a model to distinguish among fundus photos of acquired optic disc swelling due to idiopathic intracranial hypertension (IIH), non-arteritic anterior ischemic optic neuropathy (NAION), and healthy eyes.

Development and validation of a DL diagnostic algorithm.

Our model was trained and validated on 15 088 fundus photos from 5866 eyes, including eyes with IIH with a Frisén grade ≥1 (418), acute NAION (780), and healthy controls (4668). We performed external validation on an additional 1126 photos from 928 eyes across these groups. All images were obtained from randomized and nonrandomized clinical trials, publicly available datasets, and real-world clinical sources.

After preprocessing images to standardize optic disc position, we fine-tuned a ResNet-50 model. Performance was evaluated using 5-fold cross-validation, with metrics such as accuracy, area under the receiver operating characteristic curve (AUC-ROC), precision, recall, F1 scores, and confusion matrices calculated. We applied gradient-weighted class activation mapping to generate visual activation maps highlighting the regions of interest in the fundus images. External validation evaluation was performed with majority voting of our cross-validated models.

The primary outcome measures were the model's overall accuracy, class-wide AUC-ROC, precision, recall, F1 scores, and confusion matrices.

The model achieved an internal validation accuracy of 96.2%, with a macro-average AUC-ROC of 0.995. F1 scores ranged from 0.90 to 0.97 for all classes. The external validation set had an accuracy of 93.6%, F1 scores from 0.90 to 0.95, and a macro-average AUC-ROC of 0.980. Activation maps consistently highlighted the optic disc, with emphasis on the inferior optic disc for IIH, superior optic disc for NAION, and the entire optic disc for healthy eyes.

Our study demonstrates the potential of fundus-based DL models to accurately distinguish among IIH, NAION, and healthy eyes, providing a potentially valuable diagnostic method. With its strong discriminative capabilities, this model can be an important tool for neuro-ophthalmic assessment, particularly when access to specialized neuro-ophthalmologists is limited.