[Diagnostic and treatment options for retinal arterial macroaneurysms in relation to our cases]. / Retinalis artéria macroaneurysmák diagnosztikai és kezelési lehetoségei eseteink kapcsán.

In our report, we present the history of four patients diagnosed with retinal arterial macroaneurysm associated with complications. Our aim is to present the varied appearance of the disease and to present the various therapeutic options. Retinal artery macroaneurysm is a rare, but potentially vision-threatening ophthalmic condition. Macroaneurysm develops from the arteriosclerotic transformation of the artery caused by high blood pressure. Macroaneurysms can be asymptomatic, or they can be associated with exudative or hemorrhagic complication which causes visual impairment. Depending on the symptoms, they can be treated with laser photocoagulation, intravitreal injections, or with vitrectomy. Our presented cases also illustrate that each case requires individual consideration because a uniform therapeutic recommendation is still yet to be developed. In addition to the ophthalmic treatment, it is extremely important to refer the patient to internal medicine. Orv Hetil. 2023; 164(42): 1673-1677.

Human Thelaziosis Caused by Thelazia callipaeda Eyeworm, Hungary.

Ocular infections with Thelazia callipaeda eyeworms in Europe have become more common. We report a case in Hungary caused by T. callipaeda eyeworms in a 45-year-old woman who had no travel history abroad.

Automatic segmentation of hyperreflective foci in OCT images.

BACKGROUND AND OBJECTIVE: The leading cause of vision loss in the Western World is Age-related Macular Degeneration (AMD), but together with modern medicines, tracking the number of Hyperreflective Foci (HF) on Optical Coherence Tomography (OCT) images should assist the treatment of patients. Here, we developed a framework based on deep learning for the automatic segmentation of HF in OCT images. METHODS: We collected OCT images and annotated them, then these images underwent image preprocessing, and feature extraction steps. Using the prepared data we trained different types of Conventional-, Deep- and Convolutional Neural Networks to perform the task of the automatic segmentation of HF. RESULTS: We evaluated the various Neural Networks, by performing HF segmentation of clinical data belonging to patients, whose data were excluded from the training process. The results suggest that our systems can achieve reasonably high Dice Coefficient values, and they are comparable with (i.e., in most cases above 95%) the similarity between manual annotations performed by different physicians. CONCLUSION: From the results, it can be concluded that neural networks can be used to accurately segment HF in OCT images. The results are sufficiently accurate for us to incorporate them into the next phase of the research, building a decision support system for everyday clinical practice.