Ophthalmology

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

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Multimodal tactile sensing fused with vision for dexterous robotic housekeeping.

As robots are increasingly participating in our daily lives, the quests to mimic human abilities hav...

Metadata-enhanced contrastive learning from retinal optical coherence tomography images.

Deep learning has potential to automate screening, monitoring and grading of disease in medical imag...

Physics-informed deep generative learning for quantitative assessment of the retina.

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and ...

A review of ophthalmology education in the era of generative artificial intelligence.

PURPOSE: To explore the integration of generative AI, specifically large language models (LLMs), in ...

Bioethics Principles in Machine Learning-Healthcare Application Design: Achieving Health Justice and Health Equity.

Health technologies featuring artificial intelligence (AI) are becoming more common. Some healthcare...

Transfer learning may explain pigeons' ability to detect cancer in histopathology.

Pigeons' unexpected competence in learning to categorize unseen histopathological images has remaine...

Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

PURPOSE: Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive....

A generalist vision-language foundation model for diverse biomedical tasks.

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalitie...

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), freq...

DEAF-Net: Detail-Enhanced Attention Feature Fusion Network for Retinal Vessel Segmentation.

Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. ...

Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning.

Glaucoma is one of the most common causes of blindness in the world. Screening glaucoma from retinal...

Artificial Intelligence and Ophthalmic Clinical Registries.

PURPOSE: The recent advances in artificial intelligence (AI) represent a promising solution to incre...

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients ...

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning.

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged ...

Fusing multi-scale functional connectivity patterns via Multi-Branch Vision Transformer (MB-ViT) for macaque brain age prediction.

Brain age (BA) is defined as a measure of brain maturity and could help characterize both the typica...

Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images.

Within the scope of this investigation, we carried out experiments to investigate the potential of t...

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

BACKGROUND: Optic disc drusen (ODD) represent an important differential diagnosis of papilledema cau...

Soul: An OCTA dataset based on Human Machine Collaborative Annotation Framework.

Branch retinal vein occlusion (BRVO) is the most prevalent retinal vascular disease that constitutes...

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