BACKGROUND: Diabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therape...
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 1, 2024
BackgroundThe primary recognition of diabetic retinopathy (DR) is a pivotal requirement to prevent blindness and vision impairment. This deadly condition is identified by highly qualified professionals by examining colored retinal images.ObjectiveThe...
AIM: Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intel...
BMC medical informatics and decision making
Nov 11, 2024
BACKGROUND: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of ...
Accurate multi-lesion segmentation together with automated grading on fundus images played a vital role in diagnosing and treating diabetic retinopathy (DR). Nevertheless, the intrinsic patterns of fundus lesions aggravated challenges in DR detection...
This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People's Liberation Army ...
BACKGROUND: In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented.
PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.
PURPOSE: To validate the performance of autonomous diabetic retinopathy (DR) grading by comparing a human grader and a self-developed deep-learning (DL) algorithm with gold-standard evaluation.
The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilize...
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