Ophthalmology

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

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A generalised computer vision model for improved glaucoma screening using fundus images.

IMPORTANCE: Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is pa...

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.

Self-supervised learning has become the cornerstone of building generalizable and transferable artif...

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.

OBJECTIVE: To conduct a systematic review of the computer vision applications that detect, diagnose,...

Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.

BACKGROUND: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Mac...

Foundation models in ophthalmology: opportunities and challenges.

PURPOSE OF REVIEW: Last year marked the development of the first foundation model in ophthalmology, ...

Exploring the interplay of clinical reasoning and artificial intelligence in psychiatry: Current insights and future directions.

For many years, it has been widely accepted in the psychiatric field that clinical practice cannot b...

GradToken: Decoupling tokens with class-aware gradient for visual explanation of Transformer network.

Transformer networks have been widely used in the fields of computer vision, natural language proces...

Advances in Imaging-Based Machine Learning and Therapeutic Technology in the Management of Retinal Diseases.

Retinal conditions like age-related macular degeneration (AMD), diabetic retinopathy, central serous...

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

The image-based detection and classification of plant diseases has become increasingly important to ...

Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

Understanding the effect of single-missense mutations on protein stability is crucial for clinical d...

Prediction of fellow eye neovascularization in type 3 macular neovascularization (Retinal angiomatous proliferation) using deep learning.

PURPOSE: To establish a deep learning artificial intelligence model to predict the risk of long-term...

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to...

An inherently interpretable deep learning model for local explanations using visual concepts.

Over the past decade, deep learning has become the leading approach for various computer vision task...

A Recognition System for Diagnosing Salivary Gland Neoplasms Based on Vision Transformer.

Salivary gland neoplasms (SGNs) represent a group of human neoplasms characterized by a remarkable c...

Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.

Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is...

Evaluating and enhancing the robustness of vision transformers against adversarial attacks in medical imaging.

Deep neural networks (DNNs) have demonstrated exceptional performance in medical image analysis. How...

Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.

PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting an...

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death a...

A look at the emerging trends of large language models in ophthalmology.

PURPOSE OF REVIEW: As the surge in large language models (LLMs) and generative artificial intelligen...

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