AIMC Topic: Retina

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Multi-task deep learning for predicting metabolic syndrome from retinal fundus images in a Japanese health checkup dataset.

PloS one
BACKGROUND: Retinal fundus images provide a noninvasive window into systemic health, offering opportunities for early detection of metabolic disorders such as metabolic syndrome (METS).

Diabetic retinopathy classification using a multi-attention residual refinement architecture.

Scientific reports
Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading to blurred vision and even blindness. We propose a multi-attention residual refinement architecture that enhances conventional CNN performance through...

AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images.

PloS one
Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if left untreated. Regular monitoring is crucial for detecting early signs of referable DR, and the progression to moderate to severe non-proliferative D...

Retinal image-based disease classification using hybrid deep architecture with improved image features.

International ophthalmology
OBJECTIVE: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidenti...

Fluid-SegNet: Multi-dimensional loss-driven Y-Net with dilated convolutions for OCT B-scan fluid segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical Coherence Tomography (OCT) is a widely utilized imaging modality in clinical ophthalmology, particularly for retinal imaging. B-scan is a two-dimensional slice of the OCT volume. It enables high-resolution cross-sectional visualization of ret...

Efficacy of image similarity as a metric for augmenting small dataset retinal image segmentation.

Computers in biology and medicine
Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthetic image quality is the Fréchet Inception Distance (FID) which measure...

Multi-modal classification of retinal disease based on convolutional neural network.

Biomedical physics & engineering express
Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) imag...

An explainable transformer model for Alzheimer's disease detection using retinal imaging.

Scientific reports
Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide. In the absence of effective treatment options, early diagnosis is crucial for initiating management strategies to delay disease onset and slow down its progress...

Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.

International ophthalmology
PURPOSE: Stargardt disease, also called ABCA4-related retinopathy (ABCA4R), is the most common form of juvenile-onset macular dystrophy and yet lacks an FDA approved treatment. Substantial progress has been made through landmark studies like that of ...

Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images.

Scientific reports
Diabetic retinopathy (DR) is an age-related macular degeneration eye disease problem that causes pathological changes in the retinal neural and vascular system. Recently, fundus imaging is a popular technology and widely used for clinical diagnosis, ...