ArticleDiagnostics (Basel, Switzerland)2024
Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Explainable Neutrosophic Knowledge Distillation Model for Ocular Disease Classification Using Ultra-Wide Field Fundus Images.Bioengineering (Basel, Switzerland) · 2026Article
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- Application of smooth OWA operators to classification of retinitis pigmentosa.Scientific reports · 2026Article
- VG-RETFound: a hybrid retinal AI framework for early vision impairment screening through vessel-graph learning and cross-modal fusion.Frontiers in medicine · 2026Article
- Transfer learning and vision transformer for the automatic diagnosis of cataracts in ophthalmological images.Frontiers in digital health · 2026Article
- Enhanced diagnosis of diabetic retinopathy: integrating advanced algorithms for automated detection and classification.International journal of ophthalmology · 2026Article
- Intelligent retinal disease detection using deep learning.Scientific reports · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- Ultra-wide-field fundus photography and AI-based screening and referral for multiple ocular fundus diseases.Cell reports. Medicine · 2025Article
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- Multimodal AI diagnostic system for neuromyelitis optica based on ultrawide-field fundus photography.Frontiers in medicine · 2025Article
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs.Frontiers in cell and developmental biology · 2024Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
- Fundus image classification using feature concatenation for early diagnosis of retinal disease.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931-0.974). Additionally, the paper presents visualizations of the model's predictions, including confidence scores and heatmaps that highlight the model's focal points-particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning.
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.