Latest AI and machine learning research in ophthalmology for healthcare professionals.
Recently, Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on instruction-following tasks by integrating pretrained visual encoders with large language models (LLMs). However, existing approaches often struggle to ground fine-grained visual concepts in complex scenes. In this paper, we propose MoDA (Modulation Adapter), a lightweight yet effective module designed...
The Pedestrian Attribute Recognition (PAR) task aims to identify various detailed attributes of an individual, such as clothing, accessories, and gender. To enhance PAR performance, a model must capture features ranging from coarse-grained global attributes (e.g., for identifying gender) to fine-grained local details (e.g., for recognizing accessories) that may appear in diverse regions. Recent ...
Steering vision foundation models at inference time without retraining or access to large labeled datasets is a desirable yet challenging objective,...
PURPOSE: Quantifying vascular leakage in fundus fluorescein angiography (FFA) is a critical endpoint in preclinical models of diseases such as neovasc...
PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-of-distribution (OOD) data, where test data differ...
PURPOSE: The purpose of this study was to assess the utility of artificial intelligence (AI) assisted analysis of anterior segment optical coherence t...
We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) ...
Model reprogramming adapts pretrained models to downstream tasks by modifying only the input and output spaces. Visual reprogramming (VR) is one ins...
In this study, a shift-invariant optical pattern classification system is proposed. Optical machine learning systems have been widely studied as proce...
OBJECTIVE: Diabetes and hypertension pose significant health risks, especially when poorly managed. Retinal evaluation though fundus photography can p...
Glaucoma remains a leading cause of irreversible blindness worldwide, with early detection crucial for preventing vision loss. This study developed an...
PURPOSE: This study aimed to establish and validate a prediction model based on machine learning methods and SHAP algorithm to predict response to ant...
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active...
PURPOSE: Predicting long-term anatomical responses in neovascular age-related macular degeneration patients is critical for patient-specific managemen...
This study evaluated the potential of multimodal AI chatbots, specifically ChatGPT-4o, in assessing thyroid-associated ophthalmopathy (TAO) through th...
PURPOSE: Age-related macular degeneration (AMD) remains the leading cause of blindness in developed countries. There are many different intravitreal a...
The human eye is a vital sensory organ that is crucial for visual perception. The retina is the main component of the eye and is responsible for visua...
BACKGROUND AND OBJECTIVE: Realistic and accurate estimation of the surgery duration is one of the key factors influencing the optimization of hospital...
In certain ocular conditions, such as in eyes with keratoconus or after corneal laser surgery, Higher Order Aberrations (HOAs) may be dramatically ele...
PURPOSE: To evaluate the performance of ChatGPT in solving clinical scenarios in ophthalmology, specifically questions from the specialty exams for Re...