Ophthalmology

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

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D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deteriora...

EViT: An Eagle Vision Transformer With Bi-Fovea Self-Attention.

Owing to advancements in deep learning technology, vision transformers (ViTs) have demonstrated impr...

Machine Learning-Based Computer Vision for Depth Camera-Based Physiotherapy Movement Assessment: A Systematic Review.

Machine learning-based computer vision techniques using depth cameras have shown potential in physio...

Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians.

BACKGROUND: Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuo...

Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

INTRODUCTION: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) mod...

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI)...

Prediction of Postoperative Macular Hole Status by Automated Preoperative Retinal OCT Analysis: A Narrative Review.

Optical coherence tomography (OCT) is a non-invasive imaging modality essential for macular hole (MH...

Frequency-Assisted Local Attention in Lower Layers of Visual Transformers.

Since vision transformers excel at establishing global relationships between features, they play an ...

Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering...

Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

The retina is an important part of the eye that aids in focusing light and visual recognition to the...

Integrating language into medical visual recognition and reasoning: A survey.

Vision-Language Models (VLMs) are regarded as efficient paradigms that build a bridge between visual...

Accuracy of 7 artificial intelligence-based intraocular lens power calculation formulas in medium-long eyes: 2-center study.

OBJECTIVE: To compare accuracy of 7 artificial intelligence (AI)-based intraocular lens (IOL) power ...

Eye-gesture control of computer systems via artificial intelligence.

BACKGROUND: Artificial Intelligence (AI) offers transformative potential for human-computer interact...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, undersc...

Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers.

Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavi...

Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.

SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive ou...

Wide-field OCT volumetric segmentation using semi-supervised CNN and transformer integration.

Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in...

An enhanced denoising system for mammogram images using deep transformer model with fusion of local and global features.

Image denoising is a critical problem in low-level computer vision, where the aim is to reconstruct ...

Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has...

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