164 related articles for article (PubMed ID: 36171054)
1. Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.
Gao Z; Pan X; Shao J; Jiang X; Su Z; Jin K; Ye J
Br J Ophthalmol; 2023 Nov; 107(12):1852-1858. PubMed ID: 36171054
[TBL] [Abstract][Full Text] [Related]
2. Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.
Pan X; Jin K; Cao J; Liu Z; Wu J; You K; Lu Y; Xu Y; Su Z; Jiang J; Yao K; Ye J
Graefes Arch Clin Exp Ophthalmol; 2020 Apr; 258(4):779-785. PubMed ID: 31932886
[TBL] [Abstract][Full Text] [Related]
3. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
Gao Z; Jin K; Yan Y; Liu X; Shi Y; Ge Y; Pan X; Lu Y; Wu J; Wang Y; Ye J
Graefes Arch Clin Exp Ophthalmol; 2022 May; 260(5):1663-1673. PubMed ID: 35066704
[TBL] [Abstract][Full Text] [Related]
4. Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.
Jin K; Pan X; You K; Wu J; Liu Z; Cao J; Lou L; Xu Y; Su Z; Yao K; Ye J
Sci Rep; 2020 Sep; 10(1):15138. PubMed ID: 32934283
[TBL] [Abstract][Full Text] [Related]
5. A super-resolution method-based pipeline for fundus fluorescein angiography imaging.
Jiang Z; Yu Z; Feng S; Huang Z; Peng Y; Guo J; Ren Q; Lu Y
Biomed Eng Online; 2018 Sep; 17(1):125. PubMed ID: 30231879
[TBL] [Abstract][Full Text] [Related]
6. The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients.
Xu Y; Wang Y; Liu B; Tang L; Lv L; Ke X; Ling S; Lu L; Zou H
BMC Ophthalmol; 2019 Aug; 19(1):184. PubMed ID: 31412800
[TBL] [Abstract][Full Text] [Related]
7. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
Wang XN; Dai L; Li ST; Kong HY; Sheng B; Wu Q
Curr Eye Res; 2020 Dec; 45(12):1550-1555. PubMed ID: 32410471
[No Abstract] [Full Text] [Related]
8. Comparison of widefield swept-source optical coherence tomographic angiography and fluorescein fundus angiography for detection of retinal neovascularization with diabetic retinopathy.
Yang Y; Li F; Liu T; Jiao W; Zhao B
BMC Ophthalmol; 2023 Jul; 23(1):315. PubMed ID: 37438731
[TBL] [Abstract][Full Text] [Related]
9. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.
Xu K; Feng D; Mi H
Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750
[TBL] [Abstract][Full Text] [Related]
10. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S; Gopi VP; Palanisamy P
Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
[TBL] [Abstract][Full Text] [Related]
11. Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
Tseng VS; Chen CL; Liang CM; Tai MC; Liu JT; Wu PY; Deng MS; Lee YW; Huang TY; Chen YH
Transl Vis Sci Technol; 2020 Jul; 9(2):41. PubMed ID: 32855845
[TBL] [Abstract][Full Text] [Related]
12. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
Liu YP; Li Z; Xu C; Li J; Liang R
Artif Intell Med; 2019 Aug; 99():101694. PubMed ID: 31606108
[TBL] [Abstract][Full Text] [Related]
13. Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
Pandey PU; Ballios BG; Christakis PG; Kaplan AJ; Mathew DJ; Ong Tone S; Wan MJ; Micieli JA; Wong JCY
Br J Ophthalmol; 2024 Feb; 108(3):417-423. PubMed ID: 36720585
[TBL] [Abstract][Full Text] [Related]
14. Comparison of digital color fundus imaging and fluorescein angiographic findings for the early detection of diabetic retinopathy in young type 1 diabetic patients.
Kapsala Z; Anastasakis A; Mamoulakis D; Maniadaki I; Tsilimbaris M
J Fr Ophtalmol; 2018 Jan; 41(1):39-44. PubMed ID: 29191678
[TBL] [Abstract][Full Text] [Related]
15. Translation of Color Fundus Photography into Fluorescein Angiography Using Deep Learning for Enhanced Diabetic Retinopathy Screening.
Shi D; Zhang W; He S; Chen Y; Song F; Liu S; Wang R; Zheng Y; He M
Ophthalmol Sci; 2023 Dec; 3(4):100401. PubMed ID: 38025160
[TBL] [Abstract][Full Text] [Related]
16. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.
Ryu G; Lee K; Park D; Park SH; Sagong M
Sci Rep; 2021 Nov; 11(1):23024. PubMed ID: 34837030
[TBL] [Abstract][Full Text] [Related]
17. Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.
Li Q; Zhu XR; Sun G; Zhang L; Zhu M; Tian T; Guo C; Mazhar S; Yang JK; Li Y
Comput Math Methods Med; 2022; 2022():4316507. PubMed ID: 35966243
[TBL] [Abstract][Full Text] [Related]
18. Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment.
RĂªgo S; Dutra-Medeiros M; Soares F; Monteiro-Soares M
Ophthalmologica; 2021; 244(3):250-257. PubMed ID: 33120397
[TBL] [Abstract][Full Text] [Related]
19. Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.
Mohamed E; Elmohsen MA; Basha T
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3898-3901. PubMed ID: 34892084
[TBL] [Abstract][Full Text] [Related]
20. A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography.
Ryu G; Lee K; Park D; Kim I; Park SH; Sagong M
Transl Vis Sci Technol; 2022 Feb; 11(2):39. PubMed ID: 35703566
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]