Objective To evaluate a deep learning-based method to autonomously detect dry eye
disease (DED) in anterior segment optical coherence tomography (AS-OCT) images …

Purpose To report a multidisease deep learning diagnostic network (MDDN) of common
corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and …

Objective To assess the performance of convolutional neural networks (CNNs) for
automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on …

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a
significant impairment of quality of life. The diagnosis and treatment of the disease are often …

There is an increasing need for an objective grading system to evaluate the severity of dry
eye disease (DED). In this study, a fully automated deep learning-based system for the …

Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits
to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of …

Dry eye disease (DED) is a chronic eye disease and a common complication among the
world's population. Evaporation of moisture from tear film or a decrease in tear production …

This study explores the potential of Artificial Intelligence (AI) in early screening and
prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic …

The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains
limited due to the lack of standardized image formats and analysis models. To overcome …

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the
diagnostic criteria used and population under study. However, it remains one of the most …