Ophthalmology

Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive utility of multimodal data, including eye tracking, EEG, actigraphy, and behavioral indices, in differentiating adults with ADHD from healthy individual...

Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.

AIM: To quantitatively analyze the relationship between spherical equivalent refraction (SER) and retinal vascular changes in school-age children with refractive error by applying fundus photography combined with artificial intelligence (AI) technology and explore the structural changes in retinal vasculature in these children.

Synth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios.

Prompt learning is a powerful technique that enables the transfer of Vision-Language Models (VLMs) like CLIP to downstream tasks. However, when the pr...

DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant inf...

Ophthalmology Journals' Guidelines on Generative Artificial Intelligence: A Comprehensive Analysis.

PURPOSE: The integration of generative artificial intelligence (GAI) into scientific research and academic writing has generated considerable controve...

On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions.

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many effor...

A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.

As an alternative to assessments performed by human experts, artificial intelligence (AI) is currently being used for screening fundus images and moni...

Integration of Optical Coherence Tomography Images and Real-Life Clinical Data for Deep Learning Modeling: A Unified Approach in Prognostication of Diabetic Macular Edema.

The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) ca...

Macular patterns of neuronal and visual field loss in recovered optic neuritis identified by machine learning.

We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in reco...

Diabetes and Cataracts Development-Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study.

Diabetes has become a global epidemic, contributing to significant health challenges due to its complications. Among these, diabetes can affect sight...

Analyzing the Effect of Surgical and Corneal Parameters on the Postoperative Refractive Outcomes of Smile in Myopic Eyes Based on Machine Learning.

PURPOSE: To analyze the influence of individual parameters on the postoperative refractive outcomes of small incision lenticule extraction (SMILE) in ...

Evaluating the Influence of Clinical Data on Inter-Observer Variability in Optic Disc Analysis for AI-Assisted Glaucoma Screening.

PURPOSE: This study aims to evaluate the inter-observer variability in assessing the optic disc in fundus photographs and its implications for establi...

Artificial intelligence virtual assistants in primary eye care practice.

PURPOSE: To propose a novel artificial intelligence (AI)-based virtual assistant trained on tabular clinical data that can provide decision-making sup...

The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.

The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, ...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threate...

Machine Vision with a CMOS-Based Hyperspectral Imaging Sensor Enables Sensing Meat Freshness.

Imaging spectral information of materials and analysis of its properties have become an intriguing tool for consumer electronics used for food inspect...

CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities.

Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detect...

Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models.

PURPOSE: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using ...

3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer's disease via machine learning.

Alzheimer's Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation ...

An integrated microflow cytometry platform with artificial intelligence capabilities for point-of-care cellular phenotype analysis.

The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyping and enumeration using artificial intelligence ...