Ophthalmology

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

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A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss.

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detectin...

Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.

Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroo...

Neuroscientific insights about computer vision models: a concise review.

The development of biologically-inspired computational models has been the focus of study ever since...

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) ...

Radial polarisation patterns identify macular damage: a machine learning approach.

CLINICAL RELEVANCE: Identifying polarisation-modulated patterns may be an effective method for both ...

Effect of childhood atropine treatment on adult choroidal thickness using sequential deep learning-enabled segmentation.

PURPOSE: To describe choroidal thickness measurements using a sequential deep learning segmentation ...

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration.

INTRODUCTION: Age-related macular degeneration (AMD) is a leading cause of irreversible blindness wo...

Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis.

PURPOSE: To evaluate the accuracy of large language models (LLMs) in answering ophthalmology board-s...

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiolo...

Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.

The application of machine learning to tasks involving volumetric biomedical imaging is constrained ...

Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy.

The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color...

A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a si...

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Breast cancer is a significant global public health concern, with various treatment options availabl...

Exploring the categorical nature of colour perception: Insights from artificial networks.

The electromagnetic spectrum of light from a rainbow is a continuous signal, yet we perceive it vivi...

The role of artificial intelligence in macular hole management: A scoping review.

We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We s...

Prompt-guided and multimodal landscape scenicness assessments with vision-language models.

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to...

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; howev...

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organi...

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