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 …

Corneal diseases represent a growing public health burden, especially in resource-limited
settings lacking access to specialized eye care. Artificial intelligence (AI) offers promising …

Purpose To use artificial intelligence to identify relationships between morphological
characteristics of the Meibomian glands (MGs), subject factors, clinical outcomes, and …

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to
evaporative dry eye, significantly impacting visual quality. With a global prevalence …

Data quality plays a crucial role in computer-aided diagnosis (CAD) for ophthalmic disease
detection. Various methodologies for data enhancement and preprocessing exist, with …

Ophthalmology is a subject that highly depends on imaging examination. Artificial
intelligence (AI) technology has great potential in medical imaging analysis, including image …

In modern ophthalmology, the advent of artificial intelligence (AI) is gradually showing
promising results. The application of complex algorithms to machine and deep learning has …

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 …

Purpose: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface
Disease Index questionnaire or various objective tests, however, these diagnostic methods …

Abstract Purpose Cytarabine (Ara-C) chemotherapy causes symptoms resembling
meibomian gland dysfunction (MGD), suggesting potential associations between Ara-C and …