Ophthalmology

Artificial Intelligence in Commercial Industry: Serving the End-to-End Patient Experience Across the Digital Ecosystem.

The purpose of this article is to evaluate the application of artificial intelligence (AI) from the perspective of the orthopaedic industry with respect to the specific opportunities offered by AI. It is clear that AI has the potential to impact the entire continuum of musculoskeletal and orthopaedic care. The following areas may experience improvements from integrating AI into surgical applicatio...

Organic Synaptic Transistors Based on a Semiconductor Heterojunction for Artificial Visual and Neuromorphic Functions.

Visual acuity is the ability of the biological retina to distinguish images. High-sensitivity image acquisition improves the quality of visual perception, making images more recognizable for the visual system. Therefore, developing synaptic phototransistors with enhanced photosensitivity is crucial for high-performance artificial vision. Here, organic synaptic phototransistors (OSPs) based on p-n ...

Predicting visual field global and local parameters from OCT measurements using explainable machine learning.

Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The ...

Managerial myopia and its barrier to green innovation in high-pollution enterprises: A machine learning approach.

Green technology innovation has become a vital remedy in response to the world's growing ecological problems and the urgent need for sustainable devel...

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.

Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for...

High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.

An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent furthe...

Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome.

Glaucoma is a major cause of irreversible blindness, with primary open-angle glaucoma (POAG) being the most prevalent form. While elevated intraocula...

SignFormer-GCN: Continuous sign language translation using spatio-temporal graph convolutional networks.

Sign language is a complex visual language system that uses hand gestures, facial expressions, and body movements to convey meaning. It is the primary...

EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent.

Despite the outstanding performance of deep learning (DL) models, their interpretability remains a challenging topic. In this study, we address the tr...

Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.

PURPOSE: To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF)...

Artificial intelligence in digital pathology - time for a reality check.

The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical s...

Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis.

The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggl...

Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.

CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic acc...

Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-inva...

Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and u...

Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework.

High-frequency oscillations (HFOs) in intracranial EEG (iEEG) recordings are critical biomarkers for localizing the seizure onset zone (SOZ) in patien...

Galactose-Induced Cataracts in Rats: A Machine Learning Analysis.

Rat models are widely used to study cataracts due to their cost-effectiveness and prominent physiological and genetic similarities to humans The obje...

Ocular-induced abnormal head postures: A systematic review and analysis.

Abnormal head postures (AHPs) are frequently adopted as compensatory mechanisms by individuals affected by various ocular diseases to optimize the uti...

A novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images.

Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, c...

Bio-inspired two-stage network for efficient RGB-D salient object detection.

Recently, with the development of the Convolutional Neural Network and Vision Transformer, the detection accuracy of the RGB-D salient object detectio...