AIMC Topic: Retina

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A deep learning framework for the early detection of multi-retinal diseases.

PloS one
Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Qu...

Adaptative machine vision with microsecond-level accurate perception beyond human retina.

Nature communications
Visual adaptive devices have potential to simplify circuits and algorithms in machine vision systems to adapt and perceive images with varying brightness levels, which is however limited by sluggish adaptation process. Here, the avalanche tuning as f...

A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.

Scientific reports
The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We ...

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.

Nature communications
Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) h...

Selection of pre-trained weights for transfer learning in automated cytomegalovirus retinitis classification.

Scientific reports
Cytomegalovirus retinitis (CMVR) is a significant cause of vision loss. Regular screening is crucial but challenging in resource-limited settings. A convolutional neural network is a state-of-the-art deep learning technique to generate automatic diag...

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

Biomedical physics & engineering express
The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularizati...

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.

Medical & biological engineering & computing
Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current regist...

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers.

Artificial intelligence in medicine
Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varyi...

Association of retinal image-based, deep learning cardiac BioAge with telomere length and cardiovascular biomarkers.

Optometry and vision science : official publication of the American Academy of Optometry
SIGNIFICANCE: Our retinal image-based deep learning (DL) cardiac biological age (BioAge) model could facilitate fast, accurate, noninvasive screening for cardiovascular disease (CVD) in novel community settings and thus improve outcome with those wit...

Diagnostic application in streptozotocin-induced diabetic retinopathy rats: A study based on Raman spectroscopy and machine learning.

Journal of biophotonics
Vision impairment caused by diabetic retinopathy (DR) is often irreversible, making early-stage diagnosis imperative. Raman spectroscopy emerges as a powerful tool, capable of providing molecular fingerprints of tissues. This study employs RS to dete...