158 related articles for article (PubMed ID: 30370184)
1. Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes.
Nagasawa T; Tabuchi H; Masumoto H; Enno H; Niki M; Ohsugi H; Mitamura Y
PeerJ; 2018; 6():e5696. PubMed ID: 30370184
[TBL] [Abstract][Full Text] [Related]
2. Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.
Matsuba S; Tabuchi H; Ohsugi H; Enno H; Ishitobi N; Masumoto H; Kiuchi Y
Int Ophthalmol; 2019 Jun; 39(6):1269-1275. PubMed ID: 29744763
[TBL] [Abstract][Full Text] [Related]
3. Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.
Nagasawa T; Tabuchi H; Masumoto H; Enno H; Niki M; Ohara Z; Yoshizumi Y; Ohsugi H; Mitamura Y
Int Ophthalmol; 2019 Oct; 39(10):2153-2159. PubMed ID: 30798455
[TBL] [Abstract][Full Text] [Related]
4. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.
Ohsugi H; Tabuchi H; Enno H; Ishitobi N
Sci Rep; 2017 Aug; 7(1):9425. PubMed ID: 28842613
[TBL] [Abstract][Full Text] [Related]
5. Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study.
Zhang C; He F; Li B; Wang H; He X; Li X; Yu W; Chen Y
Graefes Arch Clin Exp Ophthalmol; 2021 Aug; 259(8):2225-2234. PubMed ID: 33538890
[TBL] [Abstract][Full Text] [Related]
6. Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy.
Nagasato D; Tabuchi H; Ohsugi H; Masumoto H; Enno H; Ishitobi N; Sonobe T; Kameoka M; Niki M; Hayashi K; Mitamura Y
J Ophthalmol; 2018; 2018():1875431. PubMed ID: 30515316
[TBL] [Abstract][Full Text] [Related]
7. Deep-learning classifier with ultrawide-field fundus ophthalmoscopy for detecting branch retinal vein occlusion.
Nagasato D; Tabuchi H; Ohsugi H; Masumoto H; Enno H; Ishitobi N; Sonobe T; Kameoka M; Niki M; Mitamura Y
Int J Ophthalmol; 2019; 12(1):94-99. PubMed ID: 30662847
[TBL] [Abstract][Full Text] [Related]
8. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR
JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.
Cai S; Parker F; Urias MG; Goldberg MF; Hager GD; Scott AW
JAMA Ophthalmol; 2021 Feb; 139(2):206-213. PubMed ID: 33377944
[TBL] [Abstract][Full Text] [Related]
10. Detecting glaucoma from multi-modal data using probabilistic deep learning.
Huang X; Sun J; Gupta K; Montesano G; Crabb DP; Garway-Heath DF; Brusini P; Lanzetta P; Oddone F; Turpin A; McKendrick AM; Johnson CA; Yousefi S
Front Med (Lausanne); 2022; 9():923096. PubMed ID: 36250081
[TBL] [Abstract][Full Text] [Related]
11. Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma.
Shin Y; Cho H; Shin YU; Seong M; Choi JW; Lee WJ
J Clin Med; 2022 Jun; 11(11):. PubMed ID: 35683577
[TBL] [Abstract][Full Text] [Related]
12. Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.
Keenan TDL; Chen Q; Peng Y; Domalpally A; Agrón E; Hwang CK; Thavikulwat AT; Lee DH; Li D; Wong WT; Lu Z; Chew EY
Ophthalmology; 2020 Dec; 127(12):1674-1687. PubMed ID: 32447042
[TBL] [Abstract][Full Text] [Related]
13. Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images.
Hirota M; Mizota A; Mimura T; Hayashi T; Kotoku J; Sawa T; Inoue K
Int Ophthalmol; 2020 Nov; 40(11):3013-3022. PubMed ID: 32594350
[TBL] [Abstract][Full Text] [Related]
14. The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.
Yoo TK; Choi JY; Seo JG; Ramasubramanian B; Selvaperumal S; Kim DW
Med Biol Eng Comput; 2019 Mar; 57(3):677-687. PubMed ID: 30349958
[TBL] [Abstract][Full Text] [Related]
15. Wide-field laser ophthalmoscopy for imaging of gas-filled eyes after macular hole surgery.
Nakao S; Arita R; Sato Y; Enaida H; Ueno A; Matsui T; Salehi-Had H; Ishibashi T; Sonoda KH
Clin Ophthalmol; 2016; 10():1623-30. PubMed ID: 27601877
[TBL] [Abstract][Full Text] [Related]
16. Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images.
Zhao X; Meng L; Su H; Lv B; Lv C; Xie G; Chen Y
Front Cell Dev Biol; 2022; 10():888268. PubMed ID: 35663399
[No Abstract] [Full Text] [Related]
17. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.
Burlina PM; Joshi N; Pekala M; Pacheco KD; Freund DE; Bressler NM
JAMA Ophthalmol; 2017 Nov; 135(11):1170-1176. PubMed ID: 28973096
[TBL] [Abstract][Full Text] [Related]
18. Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs.
Liu H; Li L; Wormstone IM; Qiao C; Zhang C; Liu P; Li S; Wang H; Mou D; Pang R; Yang D; Zangwill LM; Moghimi S; Hou H; Bowd C; Jiang L; Chen Y; Hu M; Xu Y; Kang H; Ji X; Chang R; Tham C; Cheung C; Ting DSW; Wong TY; Wang Z; Weinreb RN; Xu M; Wang N
JAMA Ophthalmol; 2019 Dec; 137(12):1353-1360. PubMed ID: 31513266
[TBL] [Abstract][Full Text] [Related]
19. A method for the automatic detection of myopia in Optos fundus images based on deep learning.
Shi Z; Wang T; Huang Z; Xie F; Song G
Int J Numer Method Biomed Eng; 2021 Jun; 37(6):e3460. PubMed ID: 33773080
[TBL] [Abstract][Full Text] [Related]
20. Effects of Hypertension, Diabetes, and Smoking on Age and Sex Prediction from Retinal Fundus Images.
Kim YD; Noh KJ; Byun SJ; Lee S; Kim T; Sunwoo L; Lee KJ; Kang SH; Park KH; Park SJ
Sci Rep; 2020 Mar; 10(1):4623. PubMed ID: 32165702
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
