Latest AI and machine learning research in ophthalmology for healthcare professionals.
Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the cross-domain nature of sketches and photos, as well as the semantic gap between seen and unseen classes. With the rapid advancements in modern large vision-language models (VLMs), traditional approaches that relied solely on small vision encoders have gradually been supplanted. However, both the traditional vision encoder-b...
Artificial intelligence (AI) is rapidly transforming surgical care, offering unprecedented capabilities in diagnostics, planning, and intraoperative guidance. However, its integration into clinical practice raises complex ethical challenges that must be addressed to ensure responsible and equitable use. This chapter aims to explore the ethical implications of AI in surgery through the lens of the ...
PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplas...
PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...
Object detection is a fundamental task in computer vision, aiming to localize and classify objects within images. Feature pyramid networks (FPNs) play...
PURPOSE: To develop a deep-learning model capable of measuring essential anterior segment (AS) parameters derived from preoperative ultrasound biomicr...
BACKGROUND: Repeated low-level red-light (RLRL) therapy has emerged as a promising non-invasive intervention for myopia control. However, the predicti...
Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning archit...
BACKGROUND AND AIMS: A limited amount of diabetic retinopathy (DR) development can be explained by traditional risk factors. This study aimed to deter...
PURPOSE: To characterize cell-type-specific transcriptional changes during human retinal aging and develop machine learning (ML) model for cellular ag...
Past research has suggested that eye movements can be used to uncover perpetrators of a crime to some extent (around 65Â % accuracy). We extended this ...
Atrial electrical remodeling spans molecular, electrical, and structural alterations that shorten refractoriness, facilitate reentry, and ultimately c...
BACKGROUND: Artificial intelligence (AI)-based predictive systems generate heat maps that highlight informative regions as proxy explanations for diag...
OBJECTIVE: Standard Automated Perimetry (SAP) is the primary method for monitoring glaucoma progression and an established functional endpoint in clin...
Thyroid-associated ophthalmopathy (TAO), the most common orbital disease in adults, is a specific autoimmune condition closely associated with thyroid...
PURPOSE: Integration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a d...
Access to eye care remains a global health priority, particularly for underserved populations in rural, Indigenous, and low-income communities. Despit...
PURPOSE: To describe the design and organizational structure of a global collaborative consortium aimed at aggregating longitudinal multimodal imaging...
Thyroid eye disease (TED), the most common adult orbital disease, can significantly impair patients' quality of life. Currently, effective diagnostic ...
Difficult-to-treat rheumatoid arthritis (D2T RA) is an emerging challenge in aging populations, where disease persistence and therapeutic failure ofte...