AIMC Topic: Retinal Diseases

Clear Filters Showing 141 to 150 of 173 articles

[The innovation and challenge of artificial intelligence in the whole process management of fundus disease].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cro...

Automated Abnormality Detection in Patient Retinal Function: A Deep Learning-Powered Electroretinogram Analysis System.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The electroretinogram (ERG) is an ophthalmic electrophysiology test designed to objectively measure the electrical response of the photoreceptor cells in the human retina. The analysis of the ERG is highly useful in evaluating various retinal disease...

PERFORMANCE ASSESSMENT OF AN ARTIFICIAL INTELLIGENCE CHATBOT IN CLINICAL VITREORETINAL SCENARIOS.

Retina (Philadelphia, Pa.)
PURPOSE: To determine how often ChatGPT is able to provide accurate and comprehensive information regarding clinical vitreoretinal scenarios. To assess the types of sources ChatGPT primarily uses and to determine whether they are hallucinated.

Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

Translational vision science & technology
PURPOSE: Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of thorough evaluation regarding their prevalence in China.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Journal of X-ray science and technology
Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of...

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.

JAMA ophthalmology
IMPORTANCE: Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning syst...

Deep learning for precision medicine: Guiding laser therapy in ischemic retinal diseases.

Cell reports. Medicine
In this issue of Cell Reports Medicine, Zhao and colleagues report a multi-tasking artificial intelligence system that can assist the whole process of fundus fluorescein angiography (FFA) imaging and reduce the reliance on retinal specialists in FFA ...

Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning.

Biomedical and environmental sciences : BES
OBJECTIVE: To develop a few-shot learning (FSL) approach for classifying optical coherence tomography (OCT) images in patients with inherited retinal disorders (IRDs).

Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images.

Translational vision science & technology
PURPOSE: This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images.

SRV-GAN: A generative adversarial network for segmenting retinal vessels.

Mathematical biosciences and engineering : MBE
In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model fo...