138 related articles for article (PubMed ID: 38420613)
21. Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.
Chen JS; Marra KV; Robles-Holmes HK; Ly KB; Miller J; Wei G; Aguilar E; Bucher F; Ideguchi Y; Coyner AS; Ferrara N; Campbell JP; Friedlander M; Nudleman E
Ophthalmol Sci; 2024; 4(1):100338. PubMed ID: 37869029
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network.
Musial G; Queener HM; Adhikari S; Mirhajianmoghadam H; Schill AW; Patel NB; Porter J
Transl Vis Sci Technol; 2020 Jul; 9(2):43. PubMed ID: 32855847
[TBL] [Abstract][Full Text] [Related]
24. Lesion-aware generative adversarial networks for color fundus image to fundus fluorescein angiography translation.
Huang K; Li M; Yu J; Miao J; Hu Z; Yuan S; Chen Q
Comput Methods Programs Biomed; 2023 Feb; 229():107306. PubMed ID: 36580822
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.
Ye Z; Saraf A; Ravipati Y; Hoebers F; Zha Y; Zapaishchykova A; Likitlersuang J; Tishler RB; Schoenfeld JD; Margalit DN; Haddad RI; Mak RH; Naser M; Wahid KA; Sahlsten J; Jaskari J; Kaski K; Mäkitie AA; Fuller CD; Aerts HJWL; Kann BH
medRxiv; 2023 Mar; ():. PubMed ID: 36945519
[TBL] [Abstract][Full Text] [Related]
27. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.
Alaa AM; Bolton T; Di Angelantonio E; Rudd JHF; van der Schaar M
PLoS One; 2019; 14(5):e0213653. PubMed ID: 31091238
[TBL] [Abstract][Full Text] [Related]
28. Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.
Nam JG; Witanto JN; Park SJ; Yoo SJ; Goo JM; Yoon SH
Eur Radiol; 2021 Dec; 31(12):9012-9021. PubMed ID: 34009411
[TBL] [Abstract][Full Text] [Related]
29. Quantification of Retinal Microvascular Density in Optical Coherence Tomographic Angiography Images in Diabetic Retinopathy.
Durbin MK; An L; Shemonski ND; Soares M; Santos T; Lopes M; Neves C; Cunha-Vaz J
JAMA Ophthalmol; 2017 Apr; 135(4):370-376. PubMed ID: 28301651
[TBL] [Abstract][Full Text] [Related]
30. Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.
Son J; Shin JY; Kim HD; Jung KH; Park KH; Park SJ
Ophthalmology; 2020 Jan; 127(1):85-94. PubMed ID: 31281057
[TBL] [Abstract][Full Text] [Related]
31. Retinal flow density by optical coherence tomography angiography is useful for detection of nonperfused areas in diabetic retinopathy.
Kaizu Y; Nakao S; Sekiryu H; Wada I; Yamaguchi M; Hisatomi T; Ikeda Y; Kishimoto J; Sonoda KH
Graefes Arch Clin Exp Ophthalmol; 2018 Dec; 256(12):2275-2282. PubMed ID: 30191299
[TBL] [Abstract][Full Text] [Related]
32. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.
Keel S; Li Z; Scheetz J; Robman L; Phung J; Makeyeva G; Aung K; Liu C; Yan X; Meng W; Guymer R; Chang R; He M
Clin Exp Ophthalmol; 2019 Nov; 47(8):1009-1018. PubMed ID: 31215760
[TBL] [Abstract][Full Text] [Related]
33. A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs.
Keenan TD; Dharssi S; Peng Y; Chen Q; Agrón E; Wong WT; Lu Z; Chew EY
Ophthalmology; 2019 Nov; 126(11):1533-1540. PubMed ID: 31358385
[TBL] [Abstract][Full Text] [Related]
34. Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validation Study.
Son KY; Ko J; Kim E; Lee SY; Kim MJ; Han J; Shin E; Chung TY; Lim DH
Ophthalmol Sci; 2022 Jun; 2(2):100147. PubMed ID: 36249697
[TBL] [Abstract][Full Text] [Related]
35. Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets.
Vaghefi E; Squirrell D; Yang S; An S; Xie L; Durbin MK; Hou H; Marshall J; Shreibati J; McConnell MV; Budoff M
Cardiovasc Digit Health J; 2024 Apr; 5(2):59-69. PubMed ID: 38765618
[TBL] [Abstract][Full Text] [Related]
36. A deep learning-based model for automatic segmentation and evaluation of corneal neovascularization using slit-lamp anterior segment images.
Chu X; Wang X; Zhang C; Liu H; Li F; Li G; Zhao S
Quant Imaging Med Surg; 2023 Oct; 13(10):6778-6788. PubMed ID: 37869308
[TBL] [Abstract][Full Text] [Related]
37. Predicting sex from retinal fundus photographs using automated deep learning.
Korot E; Pontikos N; Liu X; Wagner SK; Faes L; Huemer J; Balaskas K; Denniston AK; Khawaja A; Keane PA
Sci Rep; 2021 May; 11(1):10286. PubMed ID: 33986429
[TBL] [Abstract][Full Text] [Related]
38. Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography.
Hu Z; Niemeijer M; Abràmoff MD; Garvin MK
IEEE Trans Med Imaging; 2012 Oct; 31(10):1900-11. PubMed ID: 22759443
[TBL] [Abstract][Full Text] [Related]
39. Genome-wide Association Studies of Retinal Vessel Tortuosity Identify Numerous Novel Loci Revealing Genes and Pathways Associated With Ocular and Cardiometabolic Diseases.
Tomasoni M; Beyeler MJ; Vela SO; Mounier N; Porcu E; Corre T; Krefl D; Button AL; Abouzeid H; Lazaros K; Bochud M; Schlingemann R; Bergin C; Bergmann S
Ophthalmol Sci; 2023 Sep; 3(3):100288. PubMed ID: 37131961
[TBL] [Abstract][Full Text] [Related]
40. ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model.
Ma Y; Hao H; Xie J; Fu H; Zhang J; Yang J; Wang Z; Liu J; Zheng Y; Zhao Y
IEEE Trans Med Imaging; 2021 Mar; 40(3):928-939. PubMed ID: 33284751
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
