141 related articles for article (PubMed ID: 38114568)
1. Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.
Kalaw FGP; Cavichini M; Zhang J; Wen B; Lin AC; Heinke A; Nguyen T; An C; Bartsch DG; Cheng L; Freeman WR
Eye (Lond); 2024 Apr; 38(6):1189-1195. PubMed ID: 38114568
[TBL] [Abstract][Full Text] [Related]
2. Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.
Cavichini M; An C; Bartsch DG; Jhingan M; Amador-Patarroyo MJ; Long CP; Zhang J; Wang Y; Chan AX; Madala S; Nguyen T; Freeman WR
Transl Vis Sci Technol; 2020 Oct; 9(2):56. PubMed ID: 33173612
[TBL] [Abstract][Full Text] [Related]
3. Comparison of ultra-widefield fluorescein angiography with the Heidelberg Spectralis(®) noncontact ultra-widefield module versus the Optos(®) Optomap(®).
Witmer MT; Parlitsis G; Patel S; Kiss S
Clin Ophthalmol; 2013; 7():389-94. PubMed ID: 23458976
[TBL] [Abstract][Full Text] [Related]
4. Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.
Zhang J; Wang Y; Bartsch DG; Freeman WR; Nguyen TQ; An C
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():4086-4091. PubMed ID: 34892126
[TBL] [Abstract][Full Text] [Related]
5. Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology.
Haja SA; Mahadevappa V
Rom J Ophthalmol; 2023; 67(3):222-237. PubMed ID: 37876506
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Combining Feature Correspondence With Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images.
Ding L; Kang TD; Kuriyan AE; Ramchandran RS; Wykoff CC; Sharma G
IEEE Trans Biomed Eng; 2023 Feb; 70(2):523-532. PubMed ID: 35925847
[TBL] [Abstract][Full Text] [Related]
8. Megahertz ultra-wide-field swept-source retina optical coherence tomography compared to current existing imaging devices.
Reznicek L; Klein T; Wieser W; Kernt M; Wolf A; Haritoglou C; Kampik A; Huber R; Neubauer AS
Graefes Arch Clin Exp Ophthalmol; 2014 Jun; 252(6):1009-16. PubMed ID: 24789467
[TBL] [Abstract][Full Text] [Related]
9. Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation.
Li Q; Liu L
Comput Intell Neurosci; 2022; 2022():3500592. PubMed ID: 35733571
[TBL] [Abstract][Full Text] [Related]
10. Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm.
Wang B; Zhang Y; Wu C; Wang F
Contrast Media Mol Imaging; 2021; 2021():1673490. PubMed ID: 34858113
[TBL] [Abstract][Full Text] [Related]
11. Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.
Yassin SH; Wang Y; Freeman WR; Heinke A; Walker E; Nguyen T; Bartsch DG; An C; Borooah S
Clin Exp Ophthalmol; 2023 Jul; 51(5):446-452. PubMed ID: 37102206
[TBL] [Abstract][Full Text] [Related]
12. Fundus imaging in newborn children with wide-field scanning laser ophthalmoscope.
Magnusdottir V; Vehmeijer WB; Eliasdottir TS; Hardarson SH; Schalij-Delfos NE; Stefánsson E
Acta Ophthalmol; 2017 Dec; 95(8):842-844. PubMed ID: 28391630
[TBL] [Abstract][Full Text] [Related]
13. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
Shah P; Mishra DK; Shanmugam MP; Doshi B; Jayaraj H; Ramanjulu R
Indian J Ophthalmol; 2020 Feb; 68(2):398-405. PubMed ID: 31957737
[TBL] [Abstract][Full Text] [Related]
14. The big warp: Registration of disparate retinal imaging modalities and an example overlay of ultrawide-field photos and en-face OCTA images.
Thuma TBT; Bogovic JA; Gunton KB; Jimenez H; Negreiros B; Pulido JS
PLoS One; 2023; 18(4):e0284905. PubMed ID: 37098039
[TBL] [Abstract][Full Text] [Related]
15. Ultra-wide-field fluorescein angiography of the ocular fundus.
Manivannan A; Plskova J; Farrow A; Mckay S; Sharp PF; Forrester JV
Am J Ophthalmol; 2005 Sep; 140(3):525-7. PubMed ID: 16139004
[TBL] [Abstract][Full Text] [Related]
16. [A new approach for studying the retinal and choroidal circulation].
Yoneya S
Nippon Ganka Gakkai Zasshi; 2004 Dec; 108(12):836-61; discussion 862. PubMed ID: 15656089
[TBL] [Abstract][Full Text] [Related]
17. Assessment of diabetic retinopathy using two ultra-wide-field fundus imaging systems, the Clarus® and Optos™ systems.
Hirano T; Imai A; Kasamatsu H; Kakihara S; Toriyama Y; Murata T
BMC Ophthalmol; 2018 Dec; 18(1):332. PubMed ID: 30572870
[TBL] [Abstract][Full Text] [Related]
18. Robust Detection Model of Vascular Landmarks for Retinal Image Registration: A Two-Stage Convolutional Neural Network.
Kim GY; Kim JY; Lee SH; Kim SM
Biomed Res Int; 2022; 2022():1705338. PubMed ID: 35941970
[TBL] [Abstract][Full Text] [Related]
19. COMPARISON OF RETINAL PATHOLOGY VISUALIZATION IN MULTISPECTRAL SCANNING LASER IMAGING.
Meshi A; Lin T; Dans K; Chen KC; Amador M; Hasenstab K; Muftuoglu IK; Nudleman E; Chao D; Bartsch DU; Freeman WR
Retina; 2019 Jul; 39(7):1333-1342. PubMed ID: 29554078
[TBL] [Abstract][Full Text] [Related]
20. Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.
Olvera-Barrios A; Heeren TF; Balaskas K; Chambers R; Bolter L; Egan C; Tufail A; Anderson J
Br J Ophthalmol; 2021 Feb; 105(2):265-270. PubMed ID: 32376611
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
