Ophthalmology

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

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Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration.

BACKGROUND AND OBJECTIVE: For the augmented reality of surgical navigation, which overlays a 3D mode...

From pre-training to fine-tuning: An in-depth analysis of Large Language Models in the biomedical domain.

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Larg...

Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases.

PURPOSE: To describe the application of artificial intelligence (AI) in ophthalmic diseases and its ...

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feedi...

Amaurosis after Inferior Alveolar Nerve Block Injection in a Seven-Year-Old Girl: A Case Report and Review of the Literature.

A seven-year-old girl was referred for the treatment of her primary teeth. An inferior alveolar nerv...

Assessment of autostereoscopic perception using artificial intelligence-enhanced face tracking technology.

PURPOSE: Stereopsis, the ability of humans to perceive depth through distinct visual stimuli in each...

USCT-UNet: Rethinking the Semantic Gap in U-Net Network From U-Shaped Skip Connections With Multichannel Fusion Transformer.

Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-o...

Inspiration from Visual Ecology for Advancing Multifunctional Robotic Vision Systems: Bio-inspired Electronic Eyes and Neuromorphic Image Sensors.

In robotics, particularly for autonomous navigation and human-robot collaboration, the significance ...

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 ...

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