Ophthalmology

Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema.

Diabetic Macular Edema (DME) is a major complication of diabetic retinopathy characterized by fluid accumulation in the macula, leading to vision impairment. The standard treatment involves anti-VEGF (Vascular Endothelial Growth Factor) therapy, but approximately 36% of patients do not respond adequately, highlighting the need for more precise predictive models to guide treatment. This study aims ...

Artificial intelligence with ChatGPT 4: a large language model in support of ocular oncology cases.

PURPOSE: To evaluate ChatGPT's ability to analyze comprehensive case descriptions of patients with uveal melanoma and provide recommendations for the most appropriate management.

Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities.

BACKGROUND: Early maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems an...

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computat...

Dynamic blinking feature extraction for automated facial nerve paralysis detection.

Facial nerve paralysis (FNP) impair eyelid closure and blinking, risking ophthalmic complications and vision loss. Current detection methods primarily...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces t...

Enhanced crayfish optimization algorithm: Orthogonal refracted opposition-based learning for robotic arm trajectory planning.

In high-dimensional scenarios, trajectory planning is a challenging and computationally complex optimization task that requires finding the optimal tr...

Cost-efficient training of hyperspectral deep learning models for the detection of contaminating grains in bulk oats by fluorescent tagging.

Computer vision based on instance segmentation deep learning models offers great potential for automating many visual inspection tasks, such as the de...

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aid...

Integrating Eye Tracking With Grouped Fusion Networks for Semantic Segmentation on Mammogram Images.

Medical image segmentation has seen great progress in recent years, largely due to the development of deep neural networks. However, unlike in compute...

Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images.

Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR) and Rim-to-Disc Ratio (RDR). However, precise seg...

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning.

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage throug...

[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile].

UNLABELLED: Early detection of diabetic retinopathy is critical for preventing vision loss.

Age and gender-related changes in choroidal thickness: Insights from deep learning analysis of swept-source OCT images.

BACKGROUND: The choroid is a vital vascular layer of the eye, essential for maintaining ocular health. Understanding its structural variations, partic...

Efficient diagnosis of retinal disorders using dual-branch semi-supervised learning (DB-SSL): An enhanced multi-class classification approach.

The early diagnosis of retinal disorders is essential in preventing permanent or partial blindness. Identifying these conditions promptly guarantees e...

A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities.

Feature extraction in ML plays a crucial role in transforming raw data into a more meaningful and interpretable representation. In this study, we thor...

Automated strabismus detection and classification using deep learning analysis of facial images.

Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential fo...

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.

OBJECTIVE: To develop and validate a deep learning model for diabetic macular edema (DME) detection using color fundus imaging, which is applicable in...

Code-Free Deep Learning Glaucoma Detection on Color Fundus Images.

OBJECTIVE: Code-free deep learning (CFDL) allows clinicians with no coding experience to build their own artificial intelligence models. This study as...

Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.

BACKGROUND: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experienc...