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Background: To evaluate the classification performance of machine learning based on the 4 vessel density features of peripapillary optical coherence tomography angiography (OCT-A) for classifying healthy, nonarteritic anterior ischemic optic neuropathy (NAION), and optic neuritis (ON) eyes., Methods: Forty-five eyes of 45 NAION patients, 32 eyes of 32 ON patients, and 76 eyes of 76 healthy individuals with optic nerve head OCT-A were included. Four vessel density features of OCT-A images were developed using a threshold-based segmentation method and were integrated in 3 models of machine learning classifiers. Classification performances of support vector machine (SVM), random forest, and Gaussian Naive Bayes (GNB) models were evaluated with the area under the receiver-operating-characteristic curve (AUC) and accuracy., Results: We divided 121 images into a 70% training set and 30% test set. For ON-NAION classification, best results were achieved with 50% threshold, in which 3 classifiers (SVM, RF, and GNB) discriminated ON from NAION with an AUC of 1 and accuracy of 1. For ON-Normal classification, with 100% threshold, SVM and RF classifiers were able to discriminate normal from ON with AUCs of 1 and accuracies of 1. For NAION-normal classification, with 50% threshold, the SVM and RF classified the NAION from normal with AUC and accuracy of 1., Conclusions: ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for NAION and ON distinction., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 by North American Neuro-Ophthalmology Society.)

Competing Interests: The authors report no conflicts of interest.

This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neuritis (ON). The training/validation dataset included 750 RNFL OCT B-scans. Performance of our algorithm was evaluated on 194 OCT B-scans from 70 healthy eyes, 82 scans from 28 NAION eyes, and 84 scans of 29 ON eyes. Results were compared to manual segmentation as a ground-truth and to RNFL calculations from the built-in instrument software. The Dice coefficient for the test images was 0.87. The mean average RNFL thickness using our U-Net was not different from the manually segmented best estimate and OCT machine data in control and ON eyes. In NAION eyes, while the mean average RNFL thickness using our U-Net algorithm was not different from the manual segmented value, the OCT machine data were different from the manual segmented values. In NAION eyes, the MAE of the average RNFL thickness was 1.18 ± 0.69 μm and 6.65 ± 5.37 μm in the U-Net algorithm segmentation and the conventional OCT machine data, respectively (P = 0.0001)., (© 2022. The Author(s).)

Background: In the setting of a pale optic disc, distinguishing a previous episode of optic neuritis (ON) from that of nonarteritic anterior ischemic optic neuropathy (NAION) may be difficult on clinical examination. Differences in peripapillary vascular network structures, if present, might be of diagnostic utility., Methods: Thirty-five eyes with demyelinating ON, 33 eyes with NAION, and 81 eyes of normal subjects were imaged with optical coherence tomography angiography (OCT-A) to assess peripapillary vascular density (VD). In addition, OCT was used to measure peripapillary retinal nerve fiber layer (RNFL) thickness. Areas under the receiver operating characteristic curves were used to differentiate ON vs NAION., Results: NAION eyes had significantly thinner RNFL thickness than ON eyes. Age-adjusted analysis showed that the peripapillary VD values were significantly reduced in NAION (48.3 ± 7.4%) and ON eyes (54.7 ± 6.1%) compared with healthy controls (62.1 ± 4.6%); pairwise comparisons showed statistically significant differences among all 3 groups. After adjustment for severity of optic nerve injury according to mean RNFL thickness, all VD parameters were not significantly different between ON and NAION eyes. The area under the receiver operating characteristic curves for differentiating NAION from ON eyes was similar for VD (0.75) and RNFL thickness (0.74)., Conclusions: Peripapillary VD measurement performs as well as RNFL thickness for distinguishing previous episodes ON and NAION. VD decline might be secondary to RNFL damage and, therefore, VD data have a limited role differentiating these 2 disorders.