AI Medical Compendium Topic

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Diabetic Retinopathy

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Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda.

The British journal of ophthalmology
BACKGROUND: Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.

Autonomous screening for laser photocoagulation in fundus images using deep learning.

The British journal of ophthalmology
BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of r...

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

PloS one
In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of...

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation.

Ophthalmology. Retina
PURPOSE: Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera wi...

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.

Annals of medicine
BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with ...

Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.

Eye (London, England)
BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening po...

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.

Frontiers in endocrinology
BACKGROUND: Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and A...

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

Scientific reports
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct da...

SSiT: Saliency-Guided Self-Supervised Image Transformer for Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised imag...

Recognition of diabetic retinopathy and macular edema using deep learning.

Medical & biological engineering & computing
Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual ima...