121 related articles for article (PubMed ID: 38912212)
21. Automated Detection and Classification of Telemedical Retinopathy of Prematurity Images.
Vijayalakshmi C; Sakthivel P; Vinekar A
Telemed J E Health; 2020 Mar; 26(3):354-358. PubMed ID: 31084534
[No Abstract] [Full Text] [Related]
22. Development of 3D Printed Smartphone-Based Multi-Purpose Fundus Camera (MultiScope) for Retinopathy of Prematurity.
Pugalendhi A; Ranganathan R
Ann Biomed Eng; 2021 Dec; 49(12):3323-3338. PubMed ID: 34773157
[TBL] [Abstract][Full Text] [Related]
23. Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks.
Li P; Liu J
Transl Vis Sci Technol; 2022 May; 11(5):17. PubMed ID: 35579887
[TBL] [Abstract][Full Text] [Related]
24. ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network.
Hou N; Shi J; Ding X; Nie C; Wang C; Wan J
Phys Med Biol; 2023 Oct; 68(20):. PubMed ID: 37619572
[No Abstract] [Full Text] [Related]
25. Automated retinopathy of prematurity screening using deep neural networks.
Wang J; Ju R; Chen Y; Zhang L; Hu J; Wu Y; Dong W; Zhong J; Yi Z
EBioMedicine; 2018 Sep; 35():361-368. PubMed ID: 30166272
[TBL] [Abstract][Full Text] [Related]
26. Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.
Ebrahimi B; Le D; Abtahi M; Dadzie AK; Lim JI; Chan RVP; Yao X
Biomed Opt Express; 2023 Sep; 14(9):4713-4724. PubMed ID: 37791267
[TBL] [Abstract][Full Text] [Related]
27. Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.
Yao X; Dadzie A; Iddir S; Abtahi M; Ebrahimi B; Le D; Ganesh S; Son T; Heiferman M
Res Sq; 2023 Nov; ():. PubMed ID: 37986860
[TBL] [Abstract][Full Text] [Related]
28. Automated identification of retinopathy of prematurity by image-based deep learning.
Tong Y; Lu W; Deng QQ; Chen C; Shen Y
Eye Vis (Lond); 2020; 7():40. PubMed ID: 32766357
[TBL] [Abstract][Full Text] [Related]
29. Effective field of view of wide-field fundus photography in the Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP).
Ji MH; Zaidi M; Bodnar Z; Wang SK; Kumm J; Moshfeghi DM
Sci Rep; 2022 Nov; 12(1):19276. PubMed ID: 36369465
[TBL] [Abstract][Full Text] [Related]
30. Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening.
Wang J; Ji J; Zhang M; Lin JW; Zhang G; Gong W; Cen LP; Lu Y; Huang X; Huang D; Li T; Ng TK; Pang CP
JAMA Netw Open; 2021 May; 4(5):e218758. PubMed ID: 33950206
[TBL] [Abstract][Full Text] [Related]
31. Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.
Hanif A; Yıldız İ; Tian P; Kalkanlı B; Erdoğmuş D; Ioannidis S; Dy J; Kalpathy-Cramer J; Ostmo S; Jonas K; Chan RVP; Chiang MF; Campbell JP
Ophthalmol Sci; 2022 Jun; 2(2):100122. PubMed ID: 36249702
[TBL] [Abstract][Full Text] [Related]
32. Combining convolutional neural networks and self-attention for fundus diseases identification.
Wang K; Xu C; Li G; Zhang Y; Zheng Y; Sun C
Sci Rep; 2023 Jan; 13(1):76. PubMed ID: 36593268
[TBL] [Abstract][Full Text] [Related]
33. Non-contact smartphone-based fundus imaging compared to conventional fundus imaging: a low-cost alternative for retinopathy of prematurity screening and documentation.
Wintergerst MWM; Petrak M; Li JQ; Larsen PP; Berger M; Holz FG; Finger RP; Krohne TU
Sci Rep; 2019 Dec; 9(1):19711. PubMed ID: 31873142
[TBL] [Abstract][Full Text] [Related]
34. Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography.
Atalay E; Özalp O; Devecioğlu ÖC; Erdoğan H; İnce T; Yıldırım N
Turk J Ophthalmol; 2022 Jun; 52(3):193-200. PubMed ID: 35770344
[TBL] [Abstract][Full Text] [Related]
35. [Wide angle fundus documentation in retinopathy of prematurity].
Seiberth V; Woldt C
Ophthalmologe; 2001 Oct; 98(10):960-3. PubMed ID: 11699318
[TBL] [Abstract][Full Text] [Related]
36. Diagnostic Discrepancies in Retinopathy of Prematurity Classification.
Campbell JP; Ryan MC; Lore E; Tian P; Ostmo S; Jonas K; Chan RVP; Chiang MF;
Ophthalmology; 2016 Aug; 123(8):1795-1801. PubMed ID: 27238376
[TBL] [Abstract][Full Text] [Related]
37. Smartphone-based fundus photography for screening of plus-disease retinopathy of prematurity.
Patel TP; Aaberg MT; Paulus YM; Lieu P; Dedania VS; Qian CX; Besirli CG; Margolis T; Fletcher DA; Kim TN
Graefes Arch Clin Exp Ophthalmol; 2019 Nov; 257(11):2579-2585. PubMed ID: 31501929
[TBL] [Abstract][Full Text] [Related]
38. Predictors of treatment-warranted retinopathy of prematurity in the SUNDROP cohort: influence of photographic features.
Ludwig CA; Greven MA; Moshfeghi DM
Graefes Arch Clin Exp Ophthalmol; 2017 Oct; 255(10):1935-1946. PubMed ID: 28782073
[TBL] [Abstract][Full Text] [Related]
39. Detection of retinal lesions in diabetic retinopathy: comparative evaluation of 7-field digital color photography versus red-free photography.
Venkatesh P; Sharma R; Vashist N; Vohra R; Garg S
Int Ophthalmol; 2015 Oct; 35(5):635-40. PubMed ID: 22961609
[TBL] [Abstract][Full Text] [Related]
40. GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.
Attallah O
Diagnostics (Basel); 2023 Jan; 13(2):. PubMed ID: 36672981
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
