Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.

IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophth...

Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware.

Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-phys...

Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with research...

Exploring cultural imaginaries of robots with children with brittle bone disease: a participatory design study.

A symbiotic relationship exists between narrative imaginaries of and real-life advancements in techn...

Generative Adversarial Network With Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising.

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer r...

IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-Training.

In medical Vision-Language Pre-training (VLP), significant work focuses on extracting text and image...

Towards the automatic detection of activities of daily living using eye-movement and accelerometer data with neural networks.

Early diagnosis of neurodegenerative diseases, such as Alzheimer's disease, improves treatment and c...

Neural networks through the lens of evolutionary dynamics.

This article revisits Artificial Neural Networks (NNs) through the lens of Evolutionary Dynamics. Th...

Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine.

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many r...

Simignore: Exploring and enhancing multimodal large model complex reasoning via similarity computation.

Recently, the field of multimodal large language models (MLLMs) has grown rapidly, with many Large V...

Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.

AIM: To quantitatively analyze the relationship between spherical equivalent refraction (SER) and re...

DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlati...

Synth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios.

Prompt learning is a powerful technique that enables the transfer of Vision-Language Models (VLMs) l...

Ophthalmology Journals' Guidelines on Generative Artificial Intelligence: A Comprehensive Analysis.

PURPOSE: The integration of generative artificial intelligence (GAI) into scientific research and ac...

A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.

As an alternative to assessments performed by human experts, artificial intelligence (AI) is current...

On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions.

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defi...

Direct perception of affective valence from vision.

Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the v...

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