Ophthalmology

VAULT: vault accuracy using deep learning technology: new image-based artificial intelligence model for predicting implantable collamer lens postoperative vault.

PURPOSE: To develop an accurate deep learning model to predict postoperative vault of phakic implantable collamer lenses (ICLs).

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

PURPOSE: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distingu...

Integrating Machine Learning and Traditional Survival Analysis to Identify Key Predictors of Foveal Involvement in Geographic Atrophy.

PURPOSE: The purpose of this study was to investigate the incidence of foveal involvement in geographic atrophy (GA) secondary to age-related macular ...

Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

PURPOSE: The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-...

An editorial on myopia control, mainly written by ChatGPT.

Quantitative Assessment of Fundus Tessellated Density in Highly Myopic Glaucoma Using Deep Learning.

PURPOSE: To characterize the fundus tessellated density (FTD) in highly myopic glaucoma (HMG) and high myopia (HM) for discovering early signs and dia...

Artificial Intelligence in Cataract Surgery: A Systematic Review.

PURPOSE: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing catara...

Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

PURPOSE: Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of t...

Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.

In many different domains, experts can make complex decisions after glancing very briefly at an image. However, the perceptual mechanisms underlying e...

[Preliminary study on automatic quantification and grading of leopard spots fundus based on deep learning technology].

To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The ...

A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio ...

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study.

PURPOSE: To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progressi...

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

IMPORTANCE: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.

Large Language Models: A Historical and Sociocultural Perspective.

This letter explores the intricate historical and contemporary links between large language models (LLMs) and cognitive science through the lens of in...

Under AI's lens: spotting mutations visually.

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and pos...

Distinct Clinical Effects of Two RP1L1 Hotspots in East Asian Patients With Occult Macular Dystrophy (Miyake Disease): EAOMD Report 4.

PURPOSE: To characterize the clinical effects of two RP1L1 hotspots in patients with East Asian occult macular dystrophy (OMD).

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

PURPOSE: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated PO...

Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence.

Epilepsy affects over 50 million persons worldwide, with less than 50% achieving long-term success following surgery. Traditional electrophysiology si...

A neural network model for predicting the effectiveness of treatment in patients with neovascular glaucoma associated with diabetes mellitus.

INTRODUCTION: The study hypothesizes that neural networks can be an effective tool for predicting treatment outcomes in patients with diabetic neovasc...