Latest AI and machine learning research in ophthalmology for healthcare professionals.
This paper proposes an architecture of the system that provides support for collaborative research focused on analysis of data acquired using Triggerfish contact lens sensor and devices for continuous monitoring of cardiovascular system properties. The system enables application of machine learning (ML) models for glaucoma diagnosis without direct intraocular pressure measurement and independently...
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease risk assessment. This study aimed to develop a machine learning model to predict RVO risk in the general population using such tabular health data, wit...
OBJECTIVE: To clinically validate a convolutional neural network (CNN)-based Android smartphone app in the identification of topical glaucoma medicati...
Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (...
Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-inv...
Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artific...
Owing to advancements in deep learning technology, vision transformers (ViTs) have demonstrated impressive performance in various computer vision task...
BACKGROUND: Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuous viral illness, or posterior circulation stroke ...
Machine learning-based computer vision techniques using depth cameras have shown potential in physiotherapy movement assessment. However, a comprehens...
IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and di...
Optical coherence tomography (OCT) is a non-invasive imaging modality essential for macular hole (MH) management. Artificial intelligence (AI) algorit...
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory se...
The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy,...
The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in th...
Vision-Language Models (VLMs) are regarded as efficient paradigms that build a bridge between visual perception and textual interpretation. For medica...
PURPOSE: Develop and test a deep learning (DL) algorithm for detecting referable glaucoma.
BACKGROUND: Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition,...
Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introdu...
Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, underscores the importance of early detection and timely ...
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncyclo...