Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs
Objective With a growing need for ultra-widefield fundus (UWF) fundus photographs in clinics and AI development, image quality assessment (IQA) of UWF fundus photographs is an important preceding step for accurate diagnosis and clinical interpretation. This study developed deep learning (DL) models...
Saved in:
| Main Authors: | Sang Jun Park, Kyu Hyung Park, Chang Ki Yoon, Richul Oh, Un Chul Park |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2025-05-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/5/e100058.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Investigations on whether image-sharpening algorithm can improve the determination of severity of diabetic retinopathy in ultra-widefield fundus photographs
by: Jun Takeuchi, et al.
Published: (2025-04-01) -
A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs
by: Qihang Xie, et al.
Published: (2025-01-01) -
Deep learning-based classification of multiple fundus diseases using ultra-widefield images
by: Ming-Ming Duan, et al.
Published: (2025-07-01) -
Development and evaluation of a deep learning system for screening real-world multiple abnormal findings based on ultra-widefield fundus images
by: Haodong Xiao, et al.
Published: (2025-06-01) -
Automated detection of diabetic retinopathy lesions in ultra-widefield fundus images using an attention-augmented YOLOv8 framework
by: Lei-Si Hu, et al.
Published: (2025-07-01)