106 related articles for article (PubMed ID: 32704416)
21. Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.
Ramanathan A; Athikarisamy SE; Lam GC
Eye (Lond); 2023 Aug; 37(12):2518-2526. PubMed ID: 36577806
[TBL] [Abstract][Full Text] [Related]
22. Computer versus human-expert ranking of posterior pole vascular tortuosity and dilation using retinal vessel maps generated from bedside optical coherence tomography: a proof-of-concept study.
Seely KR; Freedman SF; Grace S; Weinert MC; Hong GJ; Toth CA; Grace Prakalapakorn S
J AAPOS; 2023 Dec; 27(6):351-354. PubMed ID: 37805095
[TBL] [Abstract][Full Text] [Related]
23. The scope of artificial intelligence in retinopathy of prematurity (ROP) management.
Maitra P; Shah PK; Campbell PJ; Rishi P
Indian J Ophthalmol; 2024 Jul; 72(7):931-934. PubMed ID: 38454859
[TBL] [Abstract][Full Text] [Related]
24. Retinal Vessel Automatic Segmentation Using SegNet.
Xu X; Wang Y; Liang Y; Luo S; Wang J; Jiang W; Lai X
Comput Math Methods Med; 2022; 2022():3117455. PubMed ID: 35378728
[TBL] [Abstract][Full Text] [Related]
25. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.
Redd TK; Campbell JP; Brown JM; Kim SJ; Ostmo S; Chan RVP; Dy J; Erdogmus D; Ioannidis S; Kalpathy-Cramer J; Chiang MF;
Br J Ophthalmol; 2018 Nov; ():. PubMed ID: 30470715
[TBL] [Abstract][Full Text] [Related]
26. Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system.
Liu Y; Xie H; Zhao X; Tang J; Yu Z; Wu Z; Tian R; Chen Y; Chen M; Ntentakis DP; Du Y; Chen T; Hu Y; Zhang S; Lei B; Zhang G
EPMA J; 2024 Mar; 15(1):39-51. PubMed ID: 38463622
[TBL] [Abstract][Full Text] [Related]
27. P-Score: A Reference-Image-Based Clinical Grading Scale for Vascular Change in Retinopathy of Prematurity.
Binenbaum G; Stahl A; Coyner AS; He J; Ying GS; Ostmo S; Chan RVP; Toth C; Vinekar A; Campbell JP;
Ophthalmology; 2024 May; ():. PubMed ID: 38795976
[TBL] [Abstract][Full Text] [Related]
28. Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).
Beeche C; Gezer NS; Iyer K; Almetwali O; Yu J; Zhang Y; Dhupar R; Leader JK; Pu J
Med Phys; 2023 Jan; 50(1):449-464. PubMed ID: 36184848
[TBL] [Abstract][Full Text] [Related]
29. Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN.
Sundar S; Subramanian S; Mahmud M
Diagnostics (Basel); 2024 May; 14(11):. PubMed ID: 38893619
[TBL] [Abstract][Full Text] [Related]
30. Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study.
Lai PT; Lu WL; Kuo TR; Chung CR; Han JC; Tsai RT; Horng JT
JMIR Med Inform; 2019 Nov; 7(4):e14502. PubMed ID: 31769759
[TBL] [Abstract][Full Text] [Related]
31. VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification.
Mishra S; Wang YX; Wei CC; Chen DZ; Hu XS
Front Med (Lausanne); 2021; 8():750396. PubMed ID: 34820394
[TBL] [Abstract][Full Text] [Related]
32. Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.
Kıran Yenice E; Kara C; Erdaş ÇB
Eye (Lond); 2024 Jun; ():. PubMed ID: 38918566
[TBL] [Abstract][Full Text] [Related]
33. Deep learning based retinal vessel segmentation and hypertensive retinopathy quantification using heterogeneous features cross-attention neural network.
Liu X; Tan H; Wang W; Chen Z
Front Med (Lausanne); 2024; 11():1377479. PubMed ID: 38841586
[TBL] [Abstract][Full Text] [Related]
34. Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach.
Bolón-Canedo V; Ataer-Cansizoglu E; Erdogmus D; Kalpathy-Cramer J; Fontenla-Romero O; Alonso-Betanzos A; Chiang MF
Comput Methods Programs Biomed; 2015 Oct; 122(1):1-15. PubMed ID: 26120072
[TBL] [Abstract][Full Text] [Related]
35. Characterization of Errors in Retinopathy of Prematurity Diagnosis by Ophthalmologists-in-Training in the United States and Canada.
Al-Khaled T; Patel SN; Valikodath NG; Jonas KE; Ostmo S; Allozi R; Hallak J; Campbell JP; Chiang MF; Chan RVP
J Pediatr Ophthalmol Strabismus; 2023; 60(5):337-343. PubMed ID: 36263935
[TBL] [Abstract][Full Text] [Related]
36. Detection of smoking status from retinal images; a Convolutional Neural Network study.
Vaghefi E; Yang S; Hill S; Humphrey G; Walker N; Squirrell D
Sci Rep; 2019 May; 9(1):7180. PubMed ID: 31073220
[TBL] [Abstract][Full Text] [Related]
37. Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.
Bhimavarapu U; Chintalapudi N; Battineni G
Bioengineering (Basel); 2024 Jan; 11(1):. PubMed ID: 38247933
[TBL] [Abstract][Full Text] [Related]
38. Automated detection of early-stage ROP using a deep convolutional neural network.
Huang YP; Basanta H; Kang EY; Chen KJ; Hwang YS; Lai CC; Campbell JP; Chiang MF; Chan RVP; Kusaka S; Fukushima Y; Wu WC
Br J Ophthalmol; 2021 Aug; 105(8):1099-1103. PubMed ID: 32830123
[TBL] [Abstract][Full Text] [Related]
39. Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.
Campbell JP; Singh P; Redd TK; Brown JM; Shah PK; Subramanian P; Rajan R; Valikodath N; Cole E; Ostmo S; Chan RVP; Venkatapathy N; Chiang MF; Kalpathy-Cramer J
Pediatrics; 2021 Mar; 147(3):. PubMed ID: 33637645
[TBL] [Abstract][Full Text] [Related]
40. Automated diagnosis of plus disease in retinopathy of prematurity using quantification of vessels characteristics.
Sharafi SM; Ebrahimiadib N; Roohipourmoallai R; Farahani AD; Fooladi MI; Khalili Pour E
Sci Rep; 2024 Mar; 14(1):6375. PubMed ID: 38493272
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
