Ophthalmology

Accuracy of 7 Artificial Intelligence-Based Intraocular Lens Power Calculation Formulas in Extremely Long Caucasian Eyes.

PURPOSE: To compare 7 artificial intelligence (AI)-based intraocular lens (IOL) power calculation formulas in extremely long eyes.

Teaching deep networks to see shape: Lessons from a simplified visual world.

Deep neural networks have been remarkably successful as models of the primate visual system. One crucial problem is that they fail to account for the strong shape-dependence of primate vision. Whereas humans base their judgements of category membership to a large extent on shape, deep networks rely much more strongly on other features such as color and texture. While this problem has been widely d...

A Novel Management Challenge in Age-Related Macular Degeneration: Artificial Intelligence and Expert Prediction of Geographic Atrophy.

PURPOSE: The progression of geographic atrophy (GA) secondary to age-related macular degeneration is highly variable among individuals. Prediction of ...

TSOM: Small object motion detection neural network inspired by avian visual circuit.

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an insp...

Pharmacokinetics and metabolism of ketoconazole after single ocular instillation in Sprague-Dawley rats.

BACKGROUND AND PURPOSE: Ketoconazole is limited to its conditioned oral use due to hepatic toxicity. Its ocular eye drop administration may be an opti...

Do Ophthalmology Journals Have AI Policies for Manuscript Writing?

PURPOSE: To assess the prevalence of artificial intelligence (AI) usage policies in manuscript writing in PubMed-indexed ophthalmology journals and ex...

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.

This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-in...

Machine learning model for age-related macular degeneration based on heavy metals: The National Health and Nutrition Examination Survey 2005 to 2008.

Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy meta...

A generalised computer vision model for improved glaucoma screening using fundus images.

IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In ...

Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.

BACKGROUND: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Machine Learning (ML), Multi-Layer Perceptron (MLP) a...

Foundation models in ophthalmology: opportunities and challenges.

PURPOSE OF REVIEW: Last year marked the development of the first foundation model in ophthalmology, RETFound, setting the stage for generalizable medi...

Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: Current insights and future directions.

For many years, it has been widely accepted in the psychiatric field that clinical practice cannot be reduced to finely tuned statistical prediction s...

Advances in Imaging-Based Machine Learning and Therapeutic Technology in the Management of Retinal Diseases.

Retinal conditions like age-related macular degeneration (AMD), diabetic retinopathy, central serous chorioretinopathy (CSCR), and retinal vein occlus...

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consi...

Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The im...

Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

PURPOSE: To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type ...

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

BACKGROUND: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T...

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and r...

The differences in essential facial areas for impressions between humans and deep learning models: An eye-tracking and explainable AI approach.

This study explored the facial impressions of attractiveness, dominance and sexual dimorphism using experimental and computational methods. In Study 1...