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Eye Diseases

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Modelling and Development of a Mechanical Eye for the Evaluation of Robotic Systems for Surgery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Ophthalmic surgery, which addresses critical eye diseases such as retinal disorders, remains a formidable and arduous surgical pursuit. Nevertheless, with the advent of cutting-edge robotics and automation technology, significant advancement has been...

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and the...

Smartphone Eye Examination: Artificial Intelligence and Telemedicine.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special inte...

Artificial intelligence chatbot performance in triage of ophthalmic conditions.

Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
BACKGROUND: Timely access to human expertise for affordable and efficient triage of ophthalmic conditions is inconsistent. With recent advancements in publicly available artificial intelligence (AI) chatbots, the lay public may turn to these tools fo...

A foundation model for generalizable disease detection from retinal images.

Nature
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders. However, the development of AI models requires substantial a...

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department.

American journal of ophthalmology
PURPOSE: The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic ...

Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva.

Scientific reports
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surfac...

Artificial Intelligence-based database for prediction of protein structure and their alterations in ocular diseases.

Database : the journal of biological databases and curation
The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure predict...

Automated classification of multiple ophthalmic diseases using ultrasound images by deep learning.

The British journal of ophthalmology
BACKGROUND: Ultrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ophthalmologists remains a problem. This study aims to develop a multibranch transformer network (MB...

Privacy-Preserving Federated Learning With Domain Adaptation for Multi-Disease Ocular Disease Recognition.

IEEE journal of biomedical and health informatics
As one of the effective ways of ocular disease recognition, early fundus screening can help patients avoid unrecoverable blindness. Although deep learning is powerful for image-based ocular disease recognition, the performance mainly benefits from a ...