AIMC Topic: Fundus Oculi

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Predicting myopia risk using a machine learning model based on fundus imageomics.

Scientific reports
The purpose of this study was to develop a machine learning-based model using quantitative color fundus photography (CFP) data to predict myopia risk in school-age children, based on the axial length/corneal curvature radius (AL/CR) ratio, and to ide...

A self-supervised learning method for detection of retinitis pigmentosa and Stargardt disease.

Scientific reports
Retinitis pigmentosa (RP) and Stargardt Disease (STGD) are inherited retinal diseases that can seriously affect vision. In this study, we present a novel, two-phase self-supervised learning method that addresses the challenge of limited labeled data ...

DB-SegNet: optimized framework for glaucoma detection and optic structure segmentation from retinal fundus images.

Scientific reports
Glaucoma remains one of the primary causes of irreversible blindness, characterized by gradual damage to the optic nerve, which often goes undetected until advanced stages. Accurate and early diagnosis depends heavily on precise segmentation of the o...

Broad-spectrum eye disease classification using a deep learning-based tailored software lens.

PloS one
The early and accurate classification of eye diseases is essential for preventing irreversible visual impairment. This task can be performed by deep learning approaches that automatically classify retinal fundus images according to potential illnesse...

Correlation between atherogenic index of plasma and retinal vessels in the fundus: a cross-sectional study.

European journal of medical research
BACKGROUND: The Atherogenic Index of Plasma (AIP) is a novel logarithmic index that combines fasting triglyceride and high-density lipoprotein cholesterol (HDL-C) concentrations and is associated with the burden of atherosclerosis. Currently, the non...

Leveraging fundus images for on device eye disease diagnosis with AI powered lightweight software hardware framework.

Scientific reports
Vision loss due to illness can result from various medical conditions that affect the eyes. Advanced devices like OCT and ultra-widefield retinal cameras are expensive, making them less accessible in resource-limited settings. While eye image capture...

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.

Scientific reports
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...

Efficient fusion transformer model for accurate classification of eye diseases.

Scientific reports
The automatic diagnosis model of medical image based on deep learning can improve the diagnosis efficiency and reduce the diagnosis cost. At present, there is a lack of research on special artificial intelligence models for medical image analysis of ...

Revolutionizing AMD detection Bi model CNNs and hybrid feature selection for automated grading.

Scientific reports
Age-related macular degeneration (AMD) is a common cause of vision loss in older adults. The automated grading of AMD from fundus images can aid in early detection and treatment. In this research, we propose a comprehensive framework that can enhance...

Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.

Scientific reports
Diabetes is one of the main diseases posing a threat to healthcare systems. One of the complications of diabetes is diabetic retinopathy, which, if left untreated, can lead to serious consequences such as blindness. Early detection of this disease is...