270 related articles for article (PubMed ID: 31182763)
1. Visual Field Prediction using Recurrent Neural Network.
Park K; Kim J; Lee J
Sci Rep; 2019 Jun; 9(1):8385. PubMed ID: 31182763
[TBL] [Abstract][Full Text] [Related]
2. Data Preprocessing and Augmentation Improved Visual Field Prediction of Recurrent Neural Network with Multi-Central Datasets.
Park JR; Kim S; Kim T; Jin SW; Kim JL; Shin J; Lee SU; Jang G; Hu Y; Lee JW
Ophthalmic Res; 2023; 66(1):978-991. PubMed ID: 37231880
[TBL] [Abstract][Full Text] [Related]
3. Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models.
Eslami M; Kim JA; Zhang M; Boland MV; Wang M; Chang DS; Elze T
Ophthalmol Sci; 2023 Mar; 3(1):100222. PubMed ID: 36325476
[TBL] [Abstract][Full Text] [Related]
4. Forecasting future Humphrey Visual Fields using deep learning.
Wen JC; Lee CS; Keane PA; Xiao S; Rokem AS; Chen PP; Wu Y; Lee AY
PLoS One; 2019; 14(4):e0214875. PubMed ID: 30951547
[TBL] [Abstract][Full Text] [Related]
5. Visual field prediction using a deep bidirectional gated recurrent unit network model.
Kim H; Lee J; Moon S; Kim S; Kim T; Jin SW; Kim JL; Shin J; Lee SU; Jang G; Hu Y; Park JR
Sci Rep; 2023 Jul; 13(1):11154. PubMed ID: 37429862
[TBL] [Abstract][Full Text] [Related]
6. Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics.
Thomas PBM; Chan T; Nixon T; Muthusamy B; White A
Eye (Lond); 2019 Jul; 33(7):1133-1139. PubMed ID: 30833668
[TBL] [Abstract][Full Text] [Related]
7. Glaucoma diagnostics.
Geimer SA
Acta Ophthalmol; 2013 Feb; 91 Thesis 1():1-32. PubMed ID: 23384049
[TBL] [Abstract][Full Text] [Related]
8. Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma.
Andersson S; Heijl A; Bizios D; Bengtsson B
Acta Ophthalmol; 2013 Aug; 91(5):413-7. PubMed ID: 22583841
[TBL] [Abstract][Full Text] [Related]
9. Global and pointwise rates of decay in glaucoma eyes deteriorating according to pointwise event analysis.
Nassiri N; Moghimi S; Coleman AL; Law SK; Caprioli J; Nouri-Mahdavi K
Invest Ophthalmol Vis Sci; 2013 Feb; 54(2):1208-13. PubMed ID: 23329667
[TBL] [Abstract][Full Text] [Related]
10. From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.
Medeiros FA; Jammal AA; Thompson AC
Ophthalmology; 2019 Apr; 126(4):513-521. PubMed ID: 30578810
[TBL] [Abstract][Full Text] [Related]
11. An AI approach to dynamic visual field testing.
Cho KW; Liu X; Loizou G; Wu JX
Comput Biomed Res; 1998 Jun; 31(3):143-63. PubMed ID: 9628747
[TBL] [Abstract][Full Text] [Related]
12. Comparison of Quality and Output of Different Optimal Perimetric Testing Approaches in Children With Glaucoma.
Patel DE; Cumberland PM; Walters BC; Russell-Eggitt I; Brookes J; Papadopoulos M; Khaw PT; Viswanathan AC; Garway-Heath D; Cortina-Borja M; Rahi JS;
JAMA Ophthalmol; 2018 Feb; 136(2):155-161. PubMed ID: 29285534
[TBL] [Abstract][Full Text] [Related]
13. Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).
O'Leary N; Chauhan BC; Artes PH
Invest Ophthalmol Vis Sci; 2012 Oct; 53(11):6776-84. PubMed ID: 22952123
[TBL] [Abstract][Full Text] [Related]
14. Comparison of methods to predict visual field progression in glaucoma.
Nouri-Mahdavi K; Hoffman D; Ralli M; Caprioli J
Arch Ophthalmol; 2007 Sep; 125(9):1176-81. PubMed ID: 17846355
[TBL] [Abstract][Full Text] [Related]
15. Clustering visual field test points based on rates of progression to improve the prediction of future damage.
Hirasawa K; Murata H; Hirasawa H; Mayama C; Asaoka R
Invest Ophthalmol Vis Sci; 2014 Oct; 55(11):7681-5. PubMed ID: 25342611
[TBL] [Abstract][Full Text] [Related]
16. Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder.
Berchuck SI; Mukherjee S; Medeiros FA
Sci Rep; 2019 Dec; 9(1):18113. PubMed ID: 31792321
[TBL] [Abstract][Full Text] [Related]
17. Detecting glaucomatous progression with infrequent visual field testing.
Anderson AJ; Asokan R; Murata H; Asaoka R
Ophthalmic Physiol Opt; 2018 Mar; 38(2):174-182. PubMed ID: 29315705
[TBL] [Abstract][Full Text] [Related]
18. Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.
Pohl M; Uesaka M; Demachi K; Bhusal Chhatkuli R
Comput Med Imaging Graph; 2021 Jul; 91():101941. PubMed ID: 34265553
[TBL] [Abstract][Full Text] [Related]
19. How Many Visual Fields Are Required to Precisely Predict Future Test Results in Glaucoma Patients When Using Different Trend Analyses?
Taketani Y; Murata H; Fujino Y; Mayama C; Asaoka R
Invest Ophthalmol Vis Sci; 2015 Jun; 56(6):4076-82. PubMed ID: 26114484
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.
Smits DJ; Elze T; Wang H; Pasquale LR
Semin Ophthalmol; 2019; 34(4):232-242. PubMed ID: 31132292
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]