Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Image captioning in Bengali language using visual attention.

Automatically generating image captions poses one of the most challenging applications within artifi...

A novel method for online sex sorting of silkworm pupae (Bombyx mori) using computer vision combined with deep learning.

BACKGROUND: Silkworm pupae (SP), the pupal stage of an edible insect, have strong potential in the f...

EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent.

Despite the outstanding performance of deep learning (DL) models, their interpretability remains a c...

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 centr...

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 ...

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,...

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 ...

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...

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...

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...

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

Rat models are widely used to study cataracts due to their cost-effectiveness and prominent physiol...

Benchmarking Vision Capabilities of Large Language Models in Surgical Examination Questions.

OBJECTIVE: Recent studies investigated the potential of large language models (LLMs) for clinical de...

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

Abnormal head postures (AHPs) are frequently adopted as compensatory mechanisms by individuals affec...

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 ...

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 detec...

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 ...

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 u...

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 ...

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

Sensory information transmitted to the brain activates neurons to create a series of coping behavior...

PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography.

OBJECTIVES: To develop and evaluate a deep learning model for segmentation of the coronary artery ve...

Multitask learning in minimally invasive surgical vision: A review.

Minimally invasive surgery (MIS) has revolutionized many procedures and led to reduced recovery time...

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