ArticleDiagnostics (Basel, Switzerland)2022
A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model.
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 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- Applicability study of AI attribution methods for ophthalmic image classification.Scientific reports · 2026Article
- Breakthroughs in diabetic retinopathy diagnosis and treatment using preclinical research models: current progress and future directions.Annals of medicine · 2025Review
- Retinal optical coherence tomography intensity spatial correlation features as new biomarkers for confirmed Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- A deep learning based model for diabetic retinopathy grading.Scientific reports · 2025Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Review
- Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy.Acta diabetologica · 2023Article
- Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.Sensors (Basel, Switzerland) · 2023Article
- Multiple instance learning based classification of diabetic retinopathy in weakly-labeled widefield OCTA en face images.Scientific reports · 2023Article
- Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.PloS one · 2023Article
- Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.Journal of healthcare engineering · 2023Review
- Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology.Diagnostics (Basel, Switzerland) · 2022Article
- The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.Bioengineering (Basel, Switzerland) · 2022Review
- The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Article
- The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov-Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification outputs for all twelve layers are fused using a majority voting schema for global subject diagnosis. A cohort of 188 3D-OCT subjects are used for system evaluation using different
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.