ПРИМЕНЕНИЕ ТЕХНОЛОГИЙ МАШИННОГО ОБУЧЕНИЯ ПРИ ДИАГНОСТИКЕ ГЛАУКОМЫ

Abstract

Glaucoma is a progressive eye disease that, if left untreated, can lead to permanent vision loss or total blindness. Early detection and treatment of glaucoma is critical to preventing vision loss. However, diagnosing and treating glaucoma requires extensive testing and regular monitoring, which can be time consuming and costly. Diagnosis of glaucoma is a complex process that requires high accuracy and efficiency in identifying the symptoms of the disease. In recent years, the application of machine learning technologies has become increasingly popular in the field of glaucoma diagnostics. This article provides an overview of recent research that has applied machine learning algorithms to diagnose glaucoma, including classifying glaucoma and predicting its progression. The article highlights the benefits of applying modern machine learning technologies, such as improving accuracy, efficiency and objectivity in the diagnosis of glaucoma. It also describes some of the problems and limitations of these technologies and suggests potential solutions. Overall, the article highlights the potential of deep learning technologies in diagnosing glaucoma and their role in improving patient outcomes.