Latest AI and machine learning research in ophthalmology for healthcare professionals.
Artificial intelligence in clinical medicine is widely presented as a democratising advance, bringing the interpretive capacity of leading institutions to those that lack it. This paper argues that, under prevailing market governance, AI does not democratise but compounds: it operates as a self-amplifying mechanism that converts each increment of institutional advantage into the means of acquiring...
Cortical visual prostheses aim to partially restore vision by electrically stimulating V1. Yet, their effectiveness is constrained by the limited number of simultaneously elicitable phosphenes. Identifying sparse but informative visual features for object recognition is therefore crucial to enable functional prosthetic vision. In this study, we aim to target diagnostic features for object recognit...
Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. Early detection is essential for implementing preventative measures ...
PURPOSE OF REVIEW: Artificial intelligence (AI) is being rapidly but unevenly integrated into many aspects of medicine as well as society more broadly...
BACKGROUND: Heads-up 3D surgery is becoming increasingly more important in ophthalmic microsurgery. Digital 3D visualization systems supplement tradit...
BACKGROUND: Artificial intelligence-generated health information is increasingly used by patients, but its reliability, visible transparency indicator...
Generative artificial intelligence (AI) is having a profound impact on medical education, and much of our discourse has adopted a technical lens to ex...
PURPOSE: This project aims to develop and evaluate deep learning models using orbital magnetic resonance imaging for the prediction of continuous clin...
This systematic review and meta-analysis evaluates the performance of artificial intelligence (AI)-based models for predicting the onset and progressi...
PURPOSE: To develop and validate a self-supervised vision transformer (ViT) for automated three-class classification of Normal, High Myopia (HM), and ...
Vision-language models (VLMs) represent an emerging class of multimodal artificial intelligence (AI) systems that integrate visual information with na...
BACKGROUND: Early identification of diabetic retinopathy (DR), which is a primary cause of vision impairment globally, is a crucial phasis for effecti...
PURPOSE: This study compared traditional statistical models with machine learning algorithms for predicting surgically induced astigmatism after catar...
Automated segmentation and classification of retinal arteries and veins (A/V) are pivotal for the precise morphological characterization required in t...
The exponential growth of unstructured data has presented new opportunities for leveraging computational techniques to uncover meaningful insights in ...
Age-related macular degeneration (AMD) remains a leading cause of irreversible blindness worldwide, characterized by the progressive breakdown of the ...
Surgical resection for drug-resistant focal epilepsy relies on the precise presurgical localization of the epileptogenic zone (EZ). Although [1⁸F]FDG-...
3D printing is reshaping ophthalmic biomaterials, tissue models, implants, biosensors, and drug-delivery systems, but its clinical value depends on ma...
The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rel...
Retinitis pigmentosa (RP) is a group of inherited retinal diseases that are caused by genetic defects that lead to progressive photoreceptor loss and ...