Ophthalmology

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

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Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of ba...

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predi...

Improving Acceptance to Sensory Substitution: A Study on the V2A-SS Learning Model Based on Information Processing Learning Theory.

The visual sensory organ (VSO) serves as the primary channel for transmitting external information t...

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes.

The ability to process visual stimuli rich with motion represents an essential skill for animal surv...

Estimating Periodontal Stability Using Computer Vision.

Periodontitis is a severe infection affecting oral and systemic health and is traditionally diagnose...

Retinal imaging in an era of open science and privacy protection.

Artificial intelligence (AI) holds great promise for analyzing complex data to advance patient care ...

TEDML: a new machine learning (ML) approach for predicting thyroid eye disease and identifying key biomarkers.

Thyroid eye disease (TED) features immune infiltration and metabolic dysregulation. Understanding th...

CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings.

PURPOSE: To develop and validate a novel artificial intelligence (AI)-powered video analysis system ...

A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.

Diabetic Retinopathy (DR) is a serious condition affecting diabetes people caused by hemorrhage in t...

The dawn of the revolution that will allow us to precisely describe how microbiomes function.

The community of microorganisms inhabiting a specific environment, such as the human gut - including...

Vision Mamba and xLSTM-UNet for medical image segmentation.

Deep learning-based medical image segmentation methods are generally divided into convolutional neur...

Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.

. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion manag...

Application of machine learning techniques in GlaucomAI system for glaucoma diagnosis and collaborative research support.

This paper proposes an architecture of the system that provides support for collaborative research f...

Systematic review on visual aid technologies for surgical assistant robotic devices.

This review comprehensively analyzes the modern literature on including visual aids in diverse surgi...

Deep Learning-Driven Glaucoma Medication Bottle Recognition: A Multilingual Clinical Validation Study in Patients with Impaired Vision.

OBJECTIVE: To clinically validate a convolutional neural network (CNN)-based Android smartphone app ...

Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era.

Health communication and promotion on the internet have evolved over time, driven by the development...

UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophth...

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