238 related articles for article (PubMed ID: 28842613)
1. 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]
2. A Deep Learning Model for Detecting Rhegmatogenous Retinal Detachment Using Ophthalmologic Ultrasound Images.
Wang H; Chen X; Miao X; Tang S; Lin Y; Zhang X; Chen Y; Zhu Y
Ophthalmologica; 2024; 247(1):8-18. PubMed ID: 38113861
[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. 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]
5. 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]
6. RHEGMATOGENOUS RETINAL DETACHMENT AFTER INTRAARTERIAL CHEMOTHERAPY FOR RETINOBLASTOMA: The 2016 Founders Award Lecture.
Shields CL; Say EAT; Pefkianaki M; Regillo CD; Caywood EH; Jabbour PM; Shields JA
Retina; 2017 Aug; 37(8):1441-1450. PubMed ID: 27787452
[TBL] [Abstract][Full Text] [Related]
7. Artificial intelligence using deep learning to predict the anatomical outcome of rhegmatogenous retinal detachment surgery: a pilot study.
Fung THM; John NCRA; Guillemaut JY; Yorston D; Frohlich D; Steel DHW; Williamson TH;
Graefes Arch Clin Exp Ophthalmol; 2023 Mar; 261(3):715-721. PubMed ID: 36303063
[TBL] [Abstract][Full Text] [Related]
8. Retinal breaks and rhegmatogenous retinal detachment in association with branch retinal vein occlusion.
Kir E; Saatci AO; Ozbek Z; Kaynak S; Ergin MH
Ophthalmic Surg Lasers; 1999 Apr; 30(4):285-8. PubMed ID: 10219032
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.
Cui T; Lin D; Yu S; Zhao X; Lin Z; Zhao L; Xu F; Yun D; Pang J; Li R; Xie L; Zhu P; Huang Y; Huang H; Hu C; Huang W; Liang X; Lin H
JAMA Ophthalmol; 2023 Nov; 141(11):1045-1051. PubMed ID: 37856107
[TBL] [Abstract][Full Text] [Related]
10. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.
Li Z; Guo C; Nie D; Lin D; Zhu Y; Chen C; Wu X; Xu F; Jin C; Zhang X; Xiao H; Zhang K; Zhao L; Yan P; Lai W; Li J; Feng W; Li Y; Wei Ting DS; Lin H
Commun Biol; 2020 Jan; 3(1):15. PubMed ID: 31925315
[TBL] [Abstract][Full Text] [Related]
11. Fundus autofluorescence and spectral domain optical coherence tomography as predictors for long-term functional outcome in rhegmatogenous retinal detachment.
Poulsen CD; Petersen MP; Green A; Peto T; Grauslund J
Graefes Arch Clin Exp Ophthalmol; 2019 Apr; 257(4):715-723. PubMed ID: 30617581
[TBL] [Abstract][Full Text] [Related]
12. Characteristics and pattern of rhegmatogenous retinal detachment in pakistan.
Jamil MH; Farooq N; Khan MT; Jamil AZ
J Coll Physicians Surg Pak; 2012 Aug; 22(8):501-4. PubMed ID: 22868015
[TBL] [Abstract][Full Text] [Related]
13. Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images.
Chang J; Ko A; Park SM; Choi S; Kim K; Kim SM; Yun JM; Kang U; Shin IH; Shin JY; Ko T; Lee J; Oh BL; Park KH
Am J Ophthalmol; 2020 Sep; 217():121-130. PubMed ID: 32222370
[TBL] [Abstract][Full Text] [Related]
14. Correlation between miR-148 Expression in Vitreous and Severity of Rhegmatogenous Retinal Detachment.
Tsunekawa T; Kaneko H; Takayama K; Hwang SJ; Oishi A; Nagasaka Y; Ye F; Iwase T; Nonobe N; Ueno S; Ito Y; Yasuda S; Matsuura T; Shimizu H; Suzumura A; Kataoka K; Terasaki H
Biomed Res Int; 2017; 2017():3427319. PubMed ID: 28261609
[No Abstract] [Full Text] [Related]
15. 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]
16. Ultra-widefield fundus imaging in gas-filled eyes after vitrectomy.
Inoue M; Koto T; Hirota K; Hirakata A
BMC Ophthalmol; 2017 Jul; 17(1):114. PubMed ID: 28673266
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Non-Mydriatic Ultra-Wide Field Imaging Versus Dilated Fundus Exam and Intraoperative Findings for Assessment of Rhegmatogenous Retinal Detachment.
Abadia B; Desco MC; Mataix J; Palacios E; Navea A; Calvo P; Ferreras A
Brain Sci; 2020 Aug; 10(8):. PubMed ID: 32764520
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]