ArticleDiagnostics (Basel, Switzerland)2022
Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2026Article
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- Natural antioxidant products and nanomaterial-based delivery systems for the amelioration of diabetic retinopathy: mechanisms, applications, and translational perspectives.Frontiers in immunology · 2026Review
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and ChiScientific reports · 2025Article
- An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.Scientific reports · 2024Article
- Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean AUC of 99.15%.
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