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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

1924 related items for PubMed ID: 31957737

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.
    Bellemo V, Lim ZW, Lim G, Nguyen QD, Xie Y, Yip MYT, Hamzah H, Ho J, Lee XQ, Hsu W, Lee ML, Musonda L, Chandran M, Chipalo-Mutati G, Muma M, Tan GSW, Sivaprasad S, Menon G, Wong TY, Ting DSW.
    Lancet Digit Health; 2019 May; 1(1):e35-e44. PubMed ID: 33323239
    [Abstract] [Full Text] [Related]

  • 3. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs.
    Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M.
    Diabetes Care; 2018 Dec; 41(12):2509-2516. PubMed ID: 30275284
    [Abstract] [Full Text] [Related]

  • 4. Automated Identification of Diabetic Retinopathy Using Deep Learning.
    Gargeya R, Leng T.
    Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
    [Abstract] [Full Text] [Related]

  • 5. Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy.
    Sosale B, Sosale AR, Murthy H, Sengupta S, Naveenam M.
    Indian J Ophthalmol; 2020 Feb; 68(2):391-395. PubMed ID: 31957735
    [Abstract] [Full Text] [Related]

  • 6. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR.
    JAMA; 2016 Dec 13; 316(22):2402-2410. PubMed ID: 27898976
    [Abstract] [Full Text] [Related]

  • 7. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.
    Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M.
    Invest Ophthalmol Vis Sci; 2016 Oct 01; 57(13):5200-5206. PubMed ID: 27701631
    [Abstract] [Full Text] [Related]

  • 8. Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.
    Kubin AM, Huhtinen P, Ohtonen P, Keskitalo A, Wirkkala J, Hautala N.
    Ann Med; 2024 Dec 01; 56(1):2352018. PubMed ID: 38738798
    [Abstract] [Full Text] [Related]

  • 9. Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital.
    Liu R, Li Q, Xu F, Wang S, He J, Cao Y, Shi F, Chen X, Chen J.
    Biomed Eng Online; 2022 Jul 20; 21(1):47. PubMed ID: 35859144
    [Abstract] [Full Text] [Related]

  • 10. Validation of Artificial Intelligence Algorithm in the Detection and Staging of Diabetic Retinopathy through Fundus Photography: An Automated Tool for Detection and Grading of Diabetic Retinopathy.
    Pawar B, Lobo SN, Joseph M, Jegannathan S, Jayraj H.
    Middle East Afr J Ophthalmol; 2021 Jul 20; 28(2):81-86. PubMed ID: 34759664
    [Abstract] [Full Text] [Related]

  • 11. Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.
    Rao DP, Savoy FM, Sivaraman A, Dutt S, Shahsuvaryan M, Jrbashyan N, Hambardzumyan N, Yeghiazaryan N, Das T.
    Indian J Ophthalmol; 2024 Aug 01; 72(8):1162-1167. PubMed ID: 39078960
    [Abstract] [Full Text] [Related]

  • 12. Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs.
    Gilbert MJ, Sun JK.
    Semin Ophthalmol; 2020 Nov 16; 35(7-8):325-332. PubMed ID: 33539253
    [Abstract] [Full Text] [Related]

  • 13. Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.
    Farahat Z, Zrira N, Souissi N, Bennani Y, Bencherif S, Benamar S, Belmekki M, Ngote MN, Megdiche K.
    Surv Ophthalmol; 2024 Nov 16; 69(5):707-721. PubMed ID: 38885761
    [Abstract] [Full Text] [Related]

  • 14. Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.
    Jacoba CMP, Doan D, Salongcay RP, Aquino LAC, Silva JPY, Salva CMG, Zhang D, Alog GP, Zhang K, Locaylocay KLRB, Saunar AV, Ashraf M, Sun JK, Peto T, Aiello LP, Silva PS.
    Ophthalmol Retina; 2023 Aug 16; 7(8):703-712. PubMed ID: 36924893
    [Abstract] [Full Text] [Related]

  • 15. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.
    Rajalakshmi R, Subashini R, Anjana RM, Mohan V.
    Eye (Lond); 2018 Jun 16; 32(6):1138-1144. PubMed ID: 29520050
    [Abstract] [Full Text] [Related]

  • 16. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
    Wang XN, Dai L, Li ST, Kong HY, Sheng B, Wu Q.
    Curr Eye Res; 2020 Dec 16; 45(12):1550-1555. PubMed ID: 32410471
    [Abstract] [Full Text] [Related]

  • 17. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients.
    Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S, Stratton IM, Scanlon PH, Webster L, Mann S, du Chemin A, Owen CG, Tufail A, Rudnicka AR.
    Br J Ophthalmol; 2021 May 16; 105(5):723-728. PubMed ID: 32606081
    [Abstract] [Full Text] [Related]

  • 18. The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes.
    Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda SR, Solanki K.
    Diabetes Technol Ther; 2019 Nov 16; 21(11):635-643. PubMed ID: 31335200
    [Abstract] [Full Text] [Related]

  • 19. Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.
    Grzybowski A, Rao DP, Brona P, Negiloni K, Krzywicki T, Savoy FM.
    Ophthalmic Res; 2023 Nov 16; 66(1):1286-1292. PubMed ID: 37757777
    [Abstract] [Full Text] [Related]

  • 20. Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study.
    Ming S, Xie K, Lei X, Yang Y, Zhao Z, Li S, Jin X, Lei B.
    Int Ophthalmol; 2021 Apr 16; 41(4):1291-1299. PubMed ID: 33389425
    [Abstract] [Full Text] [Related]

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