Background We report an otherwise healthy adult with macular telangiectasia with aneurysms, ischemia, and obliterated capillaries in both eyes. Methods This is a case report with a brief literature review. Case presentation A 58-year-old Iranian woman presented with a gradual decrease in vision, with recent deterioration. Past medical history was unremarkable, and best-corrected visual acuity was 3/10 in both eyes. Multimodal imaging including fundus photo, fluorescein angiography, optical coherence tomography, and optical coherence tomography angiography was carried out. In macula of both eyes, parafoveal telangiectasia, occluded vessels, capillary dropouts, and aneurysms were observed. While there were a dense circinate exudation and edema in the macula of the right eye and a thin and disorganized inner retinal layer in the left eye, the outer retina was intact in both eyes. En face optical coherence tomography angiography revealed capillary blunting and rarefaction in both superficial and deep capillary plexuses of retina. Conclusion Our case most probably represents a case of type 3 macular telangiectasia in the absence of any systemic association.

No abstract

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).