Ophthalmology

Horizontal nystagmus identification with joint SAM segmentation and time series classification.

PURPOSE: Nystagmus is an involuntary jerky eye movement. It is an oculomotor sign of asymmetry in the vestibular pathway. With the rapid advancement of deep learning and its widespread applications, numerous deep learning-based methods for nystagmus detection have been proposed based on eye movement videos. This would enhance diagnostic efficiency. We propose a horizontal nystagmus detection model...

Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.

PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning predictions of age using fundus images from teleretinal screening of patients with diabetes. DESIGN: Cross-sectional study of retinal images. SUBJECTS: Ten thousand, five hundred thirty eye images from 2939 patients with diabetes who underwent teleretinal screening at the University of California clini...

Current perspectives in cardiogenic shock.

Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heter...

The Burden of Delayed Diabetic Retinopathy Management and Use of Artificial Intelligence-Driven Screening Tools: A Systematic Literature Review.

PURPOSE: Patients with diabetic retinopathy (DR) are at risk of visual deterioration owing to systemic and financial barriers in accessing appropriate...

Agentic artificial intelligence in ophthalmology: Toward autonomous, adaptive, and ethical eye care.

Agentic artificial intelligence (AI) represents a new generation of intelligent systems capable of autonomous goal-directed reasoning, dynamic decisio...

A fine-grained fundus image dataset for cataract severity assessment and diagnosis.

Cataracts are a leading global cause of blindness. While deep learning has shown promise in aiding cataract diagnosis, existing methods typically prov...

AI-driven biomarker learning for the early diagnosis of neurodegenerative diseases: ABLEDx.

BACKGROUND: Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extr...

Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics.

The experimental determination of eutectic points is a long-established and widely used technique, but it is generally only practical for systems with...

Quantitative Assessment of Fluorescein Angiography Leakage via Deep Learning in Pediatric Uveitis: Correlation with Clinical Parameters.

PURPOSE: Develop a deep learning algorithm for automated segmentation of retinal vascular and macular edema (ME) leakage in fluorescein angiography (F...

Multimodal AI for Predicting 3- and 5-Year Risks of Myopic Choroidal Neovascularization in High Myopia.

PURPOSE: To develop artificial intelligence (AI)-based models for estimating the risk of myopic macular neovascularization (MNV) in highly myopic eyes...

Multidisciplinary Team Diagnosis and Treatment of well-differentiated thyroid carcinoma: Current Landscape and Future Prospects.

BACKGROUND: The incidence of thyroid cancer has increased markedly in recent years, largely driven by well-differentiated thyroid carcinoma (WDTC). WD...

Maximizing impact of explainable artificial intelligence in radiotherapy: a critical review.

Objective.Artificial intelligence (AI) can enable automation, improve treatment accuracy, allow for a more efficient workflow, and improve the cost-ef...

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System Without Electroencephalography.

STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-in...

Simulation and optimization of multiple permeable reactive barriers (multi-PRBs) for acid mine drainage (AMD) based on machine learning.

Multiple permeable reactive barriers (multi-PRBs) are an effective in-situ technology for acid mine drainage (AMD) treatment. However, their practical...

Real-world performance of an AI system for diabetic retinopathy screening.

Diabetic retinopathy (DR) is a leading cause of preventable blindness, and the growing global burden of diabetes is placing increasing pressure on oph...

Development of deep learning model to screen for primary open-angle glaucoma in African ancestry individuals.

Primary open-angle glaucoma (POAG) screening using artificial intelligence (AI) has emerged as a transformative method to identify undiagnosed disease...

Intravitreal faricimab in patients with refractory diabetic macular edema: 6-month fluid analysis using artificial intelligence.

PURPOSE: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. In developed countries, intravitreal (IVT) anti-vas...

Offline AI-assisted fundus imaging for preliminary screening of childhood glaucoma.

PURPOSE: To evaluate the feasibility and performance of AI-assisted portable fundus photography in children with glaucoma. METHODS: This case series d...

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models.

BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We ...

Next-generation advanced surface plasmon resonance biosensor for dopamine detection with ZnO-ag multilayer design: Machine learning optimization for high sensitivity.

The individual's physiological system needs to function effectively for a person to maintain life in good health. Deficiency of vitamins and minerals ...