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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

157 related articles for article (PubMed ID: 37372815)

  • 1. Variability in Grading Diabetic Retinopathy Using Retinal Photography and Its Comparison with an Automated Deep Learning Diabetic Retinopathy Screening Software.
    Teoh CS; Wong KH; Xiao D; Wong HC; Zhao P; Chan HW; Yuen YS; Naing T; Yogesan K; Koh VTC
    Healthcare (Basel); 2023 Jun; 11(12):. PubMed ID: 37372815
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Agreement and Diagnostic Test Accuracy on Grading Diabetic Retinopathy Using Fundus Photographs by Allied Medical Personnel at a Community Diabetic Retinopathy Screening Program in Nepal.
    Thapa R; Bajimaya S; Pradhan E; Sharma S; Kshetri BB; Paudel M; Paudyal G
    Ophthalmic Epidemiol; 2021 Dec; 28(6):509-515. PubMed ID: 33502930
    [No Abstract]   [Full Text] [Related]  

  • 3. Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.
    Zhang G; Lin JW; Wang J; Ji J; Cen LP; Chen W; Xie P; Zheng Y; Xiong Y; Wu H; Li D; Ng TK; Pang CP; Zhang M
    BMJ Open; 2022 Jul; 12(7):e060155. PubMed ID: 35902186
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.
    Hsieh YT; Chuang LM; Jiang YD; Chang TJ; Yang CM; Yang CH; Chan LW; Kao TY; Chen TC; Lin HC; Tsai CH; Chen M
    J Formos Med Assoc; 2021 Jan; 120(1 Pt 1):165-171. PubMed ID: 32307321
    [TBL] [Abstract][Full Text] [Related]  

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

  • 8. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders.
    Tufail A; Rudisill C; Egan C; Kapetanakis VV; Salas-Vega S; Owen CG; Lee A; Louw V; Anderson J; Liew G; Bolter L; Srinivas S; Nittala M; Sadda S; Taylor P; Rudnicka AR
    Ophthalmology; 2017 Mar; 124(3):343-351. PubMed ID: 28024825
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automated analysis of retinal images for detection of referable diabetic retinopathy.
    Abràmoff MD; Folk JC; Han DP; Walker JD; Williams DF; Russell SR; Massin P; Cochener B; Gain P; Tang L; Lamard M; Moga DC; Quellec G; Niemeijer M
    JAMA Ophthalmol; 2013 Mar; 131(3):351-7. PubMed ID: 23494039
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Effectiveness and safety of screening for diabetic retinopathy with two nonmydriatic digital images compared with the seven standard stereoscopic photographic fields.
    Boucher MC; Gresset JA; Angioi K; Olivier S
    Can J Ophthalmol; 2003 Dec; 38(7):557-68. PubMed ID: 14740797
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 316(22):2402-2410. PubMed ID: 27898976
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields.
    Vujosevic S; Benetti E; Massignan F; Pilotto E; Varano M; Cavarzeran F; Avogaro A; Midena E
    Am J Ophthalmol; 2009 Jul; 148(1):111-8. PubMed ID: 19406376
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach.
    Wang Y; Shi D; Tan Z; Niu Y; Jiang Y; Xiong R; Peng G; He M
    Front Med (Lausanne); 2021; 8():740987. PubMed ID: 34901058
    [No Abstract]   [Full Text] [Related]  

  • 15. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System.
    van der Heijden AA; Abramoff MD; Verbraak F; van Hecke MV; Liem A; Nijpels G
    Acta Ophthalmol; 2018 Feb; 96(1):63-68. PubMed ID: 29178249
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Inter-observer agreement in grading severity of diabetic retinopathy in wide-field fundus photographs.
    Srinivasan S; Suresh S; Chendilnathan C; Prakash V J; Sivaprasad S; Rajalakshmi R; Anjana RM; Malik RA; Kulothungan V; Raman R; Bhende M
    Eye (Lond); 2023 Apr; 37(6):1231-1235. PubMed ID: 35595962
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.
    Piatti A; Rui C; Gazzina S; Tartaglino B; Romeo F; Manti R; Doglio M; Nada E; Giorda CB
    Eur J Ophthalmol; 2024 Aug; ():11206721241272229. PubMed ID: 39109554
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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; 21(1):47. PubMed ID: 35859144
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Diagnostic test accuracy of diabetic retinopathy screening by physician graders using a hand-held non-mydriatic retinal camera at a tertiary level medical clinic.
    Piyasena MMPN; Yip JLY; MacLeod D; Kim M; Gudlavalleti VSM
    BMC Ophthalmol; 2019 Apr; 19(1):89. PubMed ID: 30961576
    [TBL] [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; 41(4):1291-1299. PubMed ID: 33389425
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.