Ophthalmology

Classification of secondary explosives with a 1D convolutional neural network technique using terahertz time-domain spectroscopy in reflection geometry.

Terahertz time-domain spectroscopy (THz-TDS) stands out as a prominent spectroscopic technique ideal for identifying explosives. The integration of THz data with machine learning models enables rapid identification and classification of explosive molecules. The paper reports the terahertz time domain spectral study of premium explosives such RDX, HMX, TNT, PETN & Tetryl in reflection geometry. We ...

Empowering Patient Autonomy: The Role of Large Language Models (LLMs) in Scaffolding Informed Consent in Medical Practice.

The principle of (respect for) patient autonomy has traditionally emphasized independence in medical decision-making, reflecting a broader commitment to epistemic individualism. However, recent philosophical work has challenged this view, suggesting that autonomous decisions are inherently dependent on epistemic and social supports. Wilkinson and Levy's "scaffolded model" of autonomy demonstrates ...

Dual-level dynamic heterogeneous graph network for video question answering.

Recently, Video Question Answering (VideoQA) has garnered considerable research interest as a pivotal task within the realm of vision-language underst...

Improvement of an eye disease detection model by using the denoising diffusion implicit model.

With rapid developments in artificial intelligence (AI), the discussion about and applications of generative AI have increased substantially. Generati...

Dissecting the clinical and pathophysiological complexity of fundus tessellation.

Fundus tessellation (FT)-also referred to as tigroid or mosaic fundus-is characterized by increased visibility of underlying choroidal vessels. While ...

Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.

Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering tran...

Detection and quantification of fluorescein angiography leakage: From manual grading to advances in machine learning.

Fluorescein angiography (FA) has long been a cornerstone for evaluating retinal vascular leakage in diseases like uveitis, diabetic retinopathy, and m...

Prediction of Response to Spinal Cord Stimulation Using Machine Learning Based on Radiomics and Patient-Reported Outcomes.

BACKGROUND AND OBJECTIVES: Chronic pain affects more patients than cancer, diabetes, and heart disease combined, resulting in high morbidity and signi...

Deep Learning-Based Detection of Papilledema on Retinal Photographs From Handheld Cameras: A Prospective Study.

BACKGROUND: Papilledema and other optic neuropathies are critical findings in neuro-ophthalmology that require timely and accurate diagnosis. This stu...

Artificial Intelligence Diagnosis of Ocular Motility Disorders From Clinical Videos.

BACKGROUND: Multimodal artificial intelligence (AI) models have recently expanded into video analysis. In ophthalmology, one exploratory application i...

Computer Vision Detects an Association Between Gross Gill Score and Ventilation Rates in Farmed Atlantic Salmon (Salmo salar).

Poor gill health compromises the health and welfare of farmed Atlantic salmon (Salmo salar) by causing respiratory distress and increased ventilation ...

Large Language Models Use in Dry Eye Disease: Perplexity AI versus ChatGPT4.

PURPOSE: To compare the utility of two large language models (LLM) in dry eye disease (DED) clinics and research. METHODS: Trained ocular surface expe...

Chronic Ocular Sequelae of Stevens-Johnson Syndrome: Recent Advances in Understanding of Patho-Physiology and Management.

BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe mucocutaneous reactions primarily triggered by drugs or inf...

Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.

PURPOSE: The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal...

Longitudinal Validation of the Artificial Intelligence Algorithm in Home OCT for Age-Related Macular Degeneration-Report 3.

PURPOSE: Longitudinal validation of the artificial intelligence-based Notal OCT Analyzer (NOA) for identification of clinically significant changes in...

Evaluation of deep learning models for anterior segment OCT image segmentation during scleral lens wear.

PURPOSE: The accurate segmentation of corneal and contact lens boundaries in anterior segment optical coherence tomography (AS-OCT) images provides es...

Deep learning in central serous chorioretinopathy.

Less than a decade has passed since deep learning (DL) was first applied in ophthalmology. With tremendous growth in this field since then, DL is expe...

Artificial intelligence in pediatric ophthalmology: a comparative study of ChatGPT-4.0 and DeepSeek-R1 performance.

Objective: This study aims to evaluate and compare the accuracy and performance of two large language models (LLMs), ChatGPT-4.0 and DeepSeek-R1, in a...

Machine Learning Model for Predicting Visual Acuity Improvement After Intrastromal Corneal Ring Surgery in Patients With Keratoconus.

BACKGROUND: Keratoconus is a progressive, degenerative corneal disease that can lead to significant visual impairment. The intrastromal ring segment i...

A Semi-supervised Reinforcement Learning Framework Incorporating the Multi-scale IncepMambaNet Network for Glaucoma Progression Prediction.

Glaucoma is the leading cause of irreversible blindness worldwide. Currently, artificial intelligence (AI) technology combined with optical coherence ...