AIMC Topic: Diabetic Retinopathy

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Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR.

Acta ophthalmologica Dec 10, 2024
PURPOSE: To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity.
Photography Adult Sensitivity and Specificity Male Middle Aged Female Reproducibility of Results Aged Humans Diabetic Retinopathy Artificial Intelligence Retrospective Studies
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Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation.

Frontiers in immunology Dec 4, 2024
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...
Nomograms Biomarkers Fatty Acid Synthase, Type I Humans Human Umbilical Vein Endothelial Cells ROC Curve Phospholipid Transfer Proteins CD4-Positive T-Lymphocytes Random Forest Diabetic Retinopathy RNA-Seq Regulated Cell Death Gene Expression Regulation Machine Learning Gene Regulatory Networks Molecular Targeted Therapy Memory T Cells
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Diabetic retinopathy detection via deep learning based dual features integrated classification model.

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...
Deep Learning Support Vector Machine Fundus Oculi Image Interpretation, Computer-Assisted Humans Diabetic Retinopathy Algorithms
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Explainable machine learning model for predicting the risk of significant liver fibrosis in patients with diabetic retinopathy.

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 ...
Humans Diabetic Retinopathy Machine Learning Female Risk Assessment Aged Liver Cirrhosis Adult Male Middle Aged
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Multi-lesion segmentation guided deep attention network for automated detection of diabetic retinopathy.

Computers in biology and medicine Nov 5, 2024
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...
Humans Diabetic Retinopathy Neural Networks, Computer Deep Learning Image Interpretation, Computer-Assisted
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An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.

Scientific reports Nov 1, 2024
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 ...
Prognosis Humans Female Decision Trees Adult Diabetic Retinopathy Diabetes Mellitus, Type 2 Risk Factors Machine Learning Cross-Sectional Studies Aged Logistic Models Algorithms Male Support Vector Machine Middle Aged
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Self-supervised based clustering for retinal optical coherence tomography images.

Eye (London, England) Oct 28, 2024
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.
Humans Diabetic Retinopathy Macular Edema Retinal Drusen Choroidal Neovascularization Cluster Analysis Tomography, Optical Coherence Retina Supervised Machine Learning
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Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.

Ophthalmology. Retina Oct 24, 2024
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.
Cross-Sectional Studies Male Middle Aged Deep Learning Female Aged Aged, 80 and over Fluorescein Angiography Visual Acuity Tomography, Optical Coherence Macula Lutea Humans Fundus Oculi Retinal Neovascularization Diabetic Retinopathy Macular Edema Retinal Vein Occlusion Algorithms Prospective Studies
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Grading of diabetic retinopathy using a pre-segmenting deep learning classification model: Validation of an automated algorithm.

Acta ophthalmologica Oct 19, 2024
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.
Humans Middle Aged Severity of Illness Index Deep Learning Retina Female Reproducibility of Results Aged Male Diabetic Retinopathy Algorithms Retrospective Studies
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Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy.

Gene Oct 18, 2024
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...
Early Diagnosis Molecular Targeted Therapy Gene Regulatory Networks Support Vector Machine Bayes Theorem Humans Diabetic Retinopathy Machine Learning Algorithms Biomarkers Molecular Docking Simulation Gene Expression Profiling
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