These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

241 related items for PubMed ID: 36931202

  • 1. A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods.
    Liu H, Teng L, Fan L, Sun Y, Li H.
    Comput Biol Med; 2023 May; 157():106750. PubMed ID: 36931202
    [Abstract] [Full Text] [Related]

  • 2. 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 20; 18(1):332. PubMed ID: 30572870
    [Abstract] [Full Text] [Related]

  • 3. Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.
    Sedova A, Hajdu D, Datlinger F, Steiner I, Neschi M, Aschauer J, Gerendas BS, Schmidt-Erfurth U, Pollreisz A.
    Eye (Lond); 2022 Mar 20; 36(3):510-516. PubMed ID: 35132211
    [Abstract] [Full Text] [Related]

  • 4. ETDRS grading with CLARUS ultra-widefield images shows agreement with 7-fields colour fundus photography.
    Santos AR, Ghate S, Lopes M, Rocha AC, Santos T, Reste-Ferreira D, Manivannan N, Foote K, Cunha-Vaz J.
    BMC Ophthalmol; 2024 Sep 03; 24(1):387. PubMed ID: 39227901
    [Abstract] [Full Text] [Related]

  • 5. Comparison of quantitative assessment and efficiency of diabetic retinopathy diagnosis using ETDRS seven-field imaging and two ultra-widefield imaging.
    Xiao Y, Huang Z, Yuan Q, Du X, Li Z, Nie X, Shi Q, Dan H, Song Z.
    Eye (Lond); 2023 Dec 03; 37(17):3558-3564. PubMed ID: 37120657
    [Abstract] [Full Text] [Related]

  • 6. Ultra-widefield color fundus photography combined with high-speed ultra-widefield swept-source optical coherence tomography angiography for non-invasive detection of lesions in diabetic retinopathy.
    Li J, Wei D, Mao M, Li M, Liu S, Li F, Chen L, Liu M, Leng H, Wang Y, Ning X, Liu Y, Dong W, Zhong J.
    Front Public Health; 2022 Dec 03; 10():1047608. PubMed ID: 36408020
    [Abstract] [Full Text] [Related]

  • 7. Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images.
    Ran J, Zhang G, Xia F, Zhang X, Xie J, Zhang H.
    Comput Biol Med; 2024 May 03; 174():108418. PubMed ID: 38593641
    [Abstract] [Full Text] [Related]

  • 8. Assessment of early diabetic retinopathy severity using ultra-widefield Clarus versus conventional five-field and ultra-widefield Optos fundus imaging.
    Xiao Y, Dan H, Du X, Michaelide M, Nie X, Wang W, Zheng M, Wang D, Huang Z, Song Z.
    Sci Rep; 2023 Oct 10; 13(1):17131. PubMed ID: 37816867
    [Abstract] [Full Text] [Related]

  • 9. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
    Romero-Oraá R, Herrero-Tudela M, López MI, Hornero R, García M.
    Comput Methods Programs Biomed; 2024 Jun 10; 249():108160. PubMed ID: 38583290
    [Abstract] [Full Text] [Related]

  • 10.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 11. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
    Wei H, Shi P, Miao J, Zhang M, Bai G, Qiu J, Liu F, Yuan W.
    Comput Biol Med; 2024 Jun 10; 175():108459. PubMed ID: 38701588
    [Abstract] [Full Text] [Related]

  • 12.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 13. Comparison of 50° handheld fundus camera versus ultra-widefield table-top fundus camera for diabetic retinopathy detection and grading.
    Midena E, Zennaro L, Lapo C, Torresin T, Midena G, Frizziero L.
    Eye (Lond); 2023 Oct 10; 37(14):2994-2999. PubMed ID: 36854818
    [Abstract] [Full Text] [Related]

  • 14. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
    Sugeno A, Ishikawa Y, Ohshima T, Muramatsu R.
    Comput Biol Med; 2021 Oct 10; 137():104795. PubMed ID: 34488028
    [Abstract] [Full Text] [Related]

  • 15.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 16. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
    Durai DBJ, Jaya T.
    Med Biol Eng Comput; 2023 Aug 10; 61(8):2091-2113. PubMed ID: 37338737
    [Abstract] [Full Text] [Related]

  • 17. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
    Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo L, Li S.
    Artif Intell Med; 2020 Aug 10; 108():101936. PubMed ID: 32972665
    [Abstract] [Full Text] [Related]

  • 18. Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images.
    Wang X, Xu M, Zhang J, Jiang L, Li L, He M, Wang N, Liu H, Wang Z.
    IEEE J Biomed Health Inform; 2022 May 10; 26(5):2216-2227. PubMed ID: 34648460
    [Abstract] [Full Text] [Related]

  • 19. Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images.
    Batool S, Gilani SO, Waris A, Iqbal KF, Khan NB, Khan MI, Eldin SM, Awwad FA.
    Sci Rep; 2023 Sep 02; 13(1):14462. PubMed ID: 37660096
    [Abstract] [Full Text] [Related]

  • 20. Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images.
    Jian M, Chen H, Tao C, Li X, Wang G.
    Comput Biol Med; 2023 Mar 02; 155():106631. PubMed ID: 36805216
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 13.