Ophthalmology

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

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A dual-branch model combining convolution and vision transformer for crop disease classification.

Computer vision holds tremendous potential in crop disease classification, but the complex texture a...

Zero-shot and few-shot multimodal plastic waste classification with vision-language models.

The construction sector is a large consumer of plastic, generating substantial volumes of plastic wa...

Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, necessitating regular sc...

The association of trimethylamine N-oxide with diabetic retinopathy Pathology: Insights from network toxicology and molecular docking analysis.

Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite, has emerged as a potential contr...

Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology.

Accurate anatomical measurements of the eyelids are essential in periorbital plastic surgery for bot...

Optimized glaucoma detection using HCCNN with PSO-driven hyperparameter tuning.

. This study is focused on creating an effective glaucoma detection system employing a Hybrid Centri...

Artificial intelligence in refractive surgery.

PURPOSE OF REVIEW: Advances in artificial intelligence have integrated into modern medicine decision...

SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal Tension Glaucoma.

PRCIS: The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field de...

Multimodal Performance of GPT-4 in Complex Ophthalmology Cases.

The integration of multimodal capabilities into GPT-4 represents a transformative leap for artifici...

Leveraging vision transformers and entropy-based attention for accurate micro-expression recognition.

Micro-expressions are difficult to fake and inherently truthful, making micro-expression recognition...

Sharper insights: Adaptive ellipse-template for robust fovea localization in challenging retinal landscapes.

Automated identification of retinal landmarks, particularly the fovea is crucial for diagnosing diab...

DGPrompt: Dual-guidance prompts generation for vision-language models.

Introducing learnable prompts into CLIP and fine-tuning them have demonstrated excellent performance...

Two-stage color fundus image registration via Keypoint Refinement and Confidence-Guided Estimation.

Color fundus images are widely used for diagnosing diseases such as Glaucoma, Cataracts, and Diabeti...

Updates on novel and traditional OCT and OCTA biomarkers in nAMD.

Predictivity of optical coherence tomography (OCT) examination for the development of neovascular ag...

Network meta-analysis of intraocular lens power calculation formulas based on artificial intelligence in short eyes.

PURPOSE: To systematically assess and compare the accuracy of artificial intelligence (AI) -based in...

Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model.

A segmentation-free 3D Convolutional Neural Network (3DCNN) model was adopted to estimate Visual Fie...

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differe...

Artificial intelligence models utilize lifestyle factors to predict dry eye related outcomes.

The purpose of this study is to examine and interpret machine learning models that predict dry eye (...

VMKLA-UNet: vision Mamba with KAN linear attention U-Net.

In the domain of medical image segmentation, while convolutional neural networks (CNNs) and Transfor...

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