AIMC Topic: Retina

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Retinal imaging in an era of open science and privacy protection.

Experimental eye research
Artificial intelligence (AI) holds great promise for analyzing complex data to advance patient care and disease research. For example, AI interpretation of retinal imaging may enable the development of noninvasive retinal biomarkers of systemic disea...

AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed.

Progress in retinal and eye research
Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic diseases from retinal images. Unlike traditional retinal disease detection AI models which are trained ...

Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

Computers in biology and medicine
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.

Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images.

Microscopy research and technique
The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in the human eye. Central serous retinopathy is a common retinal disorder in which serous detachment occu...

Wide-field OCT volumetric segmentation using semi-supervised CNN and transformer integration.

Scientific reports
Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in the retina, beyond the conventional fields of view used in current clinical OCT imaging systems. However, wide-field scans can present significant ch...

Head-mounted surgical robots are an enabling technology for subretinal injections.

Science robotics
Therapeutic protocols involving subretinal injection, which hold the promise of saving or restoring sight, are challenging for surgeons because they are at the limits of human motor and perceptual abilities. Excessive or insufficient indentation of t...

Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning.

International ophthalmology
OBJECTIVE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 m and exhibiting high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). The purpose of the model prop...

Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.

Theranostics
Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other s...

A novel deep learning framework for retinal disease detection leveraging contextual and local features cues from retinal images.

Medical & biological engineering & computing
Retinal diseases are a serious global threat to human vision, and early identification is essential for effective prevention and treatment. However, current diagnostic methods rely on manual analysis of fundus images, which heavily depends on the exp...

Diagnosis of Chronic Kidney Disease Using Retinal Imaging and Urine Dipstick Data: Multimodal Deep Learning Approach.

JMIR medical informatics
BACKGROUND: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal image...