Latest AI and machine learning research in ophthalmology for healthcare professionals.
Vision-language models (VLMs) excel on visual question answering and multimodal reasoning benchmarks. Yet their capability on ultra-resolution images - where critical evidence is tiny, subtle, spatially distant, or distributed - remains unclear. Existing evaluations largely report final-answer accuracy, offering limited insight into whether models acquire and integrate the necessary visual evidenc...
Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations. However, existing paired topological divergences (e.g., RTD) are limited by heuristic asymmetry and, more critically, unbounded scores that depend on sample size, hindering reliable cross-scenario benchmarking. To address these challenges, we develop a unified topological toolkit serving two c...
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generat...
Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological...
While Vision-Language Models (VLMs) have shown strong visual reasoning capabilities, their spatial reasoning abilities remain largely constrained to t...
Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before...
Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling ...
Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segment...
Suicidal ideation in obsessive-compulsive disorder (OCD) is common and clinically significant, yet much of the existing literature conceptualizes suic...
Background/Aims: Neovascular glaucoma (NVG) is an aggressive secondary glaucoma with limited longitudinal data. This study reports the aetiologies, tr...
Purpose To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware A...
Background: Accurate evaluation of fine motor abilities is a key aspect of neurological rehabilitation. However, conventional approaches like goniomet...
Comprehensive wiring diagrams from electron microscopy (EM) are a powerful tool to understand the inner workings of the brain. The retina is an easily...
Quantitative eye movement analysis is important for neuro- logical diagnostics, yet existing video-oculography (VOG) systems typ- ically require calib...
Efforts to restore vision via neural implants have outpaced the ability to predict what users will perceive, leaving patients and clinicians without r...
Purpose: To develop and evaluate a deep learning model that predicts optical coherence tomography (OCT)-equivalent retinal nerve fiber layer thickness...
Purpose: To predict retinal nerve fiber layer thickness (RNFLT) norms from fundus images. Methods: We selected 18,000 OCT scans and visual fields (VF)...
Adolescence is characterized by heightened reward sensitivity, novelty seeking, and risky decision-making. Prevailing neurodevelopmental frameworks ty...
Vision-based metric distance and area measurement remains challenging in large-scale outdoor environments due to long-range sensing, camera zoom, and ...
While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions. Evidential Deep Learning (EDL) ...