ReviewMedical & biological engineering & computing2022
The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review.
Review in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy and clinical performance of deep learning models for grading diabetic retinopathy: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Application of construction and validation of logistic regression model in risk prediction of non-proliferative diabetic retinopathy in type 2 diabetes mellitus.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).Frontiers in endocrinology · 2026Review
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0.Sensors (Basel, Switzerland) · 2025Review
- Innovative Approach for Diabetic Retinopathy Severity Classification: An AI-Powered Tool using CNN-Transformer Fusion.Journal of biomedical physics & engineering · 2025Article
- Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema.Scientific reports · 2024Article
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Construction of a gene signature associated with anoikis to evaluate the prognosis and immune infiltration in patients with colorectal cancer.Translational cancer research · 2024Article
- Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.Water science and technology : a journal of the International Association on Water Pollution Research · 2024Article
- Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity.Medical & biological engineering & computing · 2024Article
- CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study.Heliyon · 2023Article
- Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).Multimedia tools and applications · 2022Article
- Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a chronic eye condition that is rapidly growing due to the prevalence of diabetes. There are challenges such as the dearth of ophthalmologists, healthcare resources, and facilities that are unable to provide patients with appropriate eye screening services. As a result, deep learning (DL) has the potential to play a critical role as a powerful automated diagnostic tool in the field of ophthalmology, particularly in the early detection of DR when compared to traditional detection techniques. The DL models are known as black boxes, despite the fact that they are widely adopted. They make no attempt to explain how the model learns representations or why it makes a particular prediction. Due to the black box design architecture, DL methods make it difficult for intended end-users like ophthalmologists to grasp how the models function, preventing model acceptance for clinical usage. Recently, several studies on the interpretability of DL methods used in DR-related tasks such as DR classification and segmentation have been published. The goal of this paper is to provide a detailed overview of interpretability strategies used in DR-related tasks. This paper also includes the authors' insights and future directions in the field of DR to help the research community overcome research problems.
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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.