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 *

767 related articles for article (PubMed ID: 29178249)

  • 1. 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]  

  • 2. 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]  

  • 3. Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population.
    Shah A; Clarida W; Amelon R; Hernaez-Ortega MC; Navea A; Morales-Olivas J; Dolz-Marco R; Verbraak F; Jorda PP; van der Heijden AA; Peris Martinez C
    J Diabetes Sci Technol; 2021 May; 15(3):655-663. PubMed ID: 32174153
    [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. Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting.
    Verbraak FD; Abramoff MD; Bausch GCF; Klaver C; Nijpels G; Schlingemann RO; van der Heijden AA
    Diabetes Care; 2019 Apr; 42(4):651-656. PubMed ID: 30765436
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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; 66(1):1286-1292. PubMed ID: 37757777
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.
    Tang F; Luenam P; Ran AR; Quadeer AA; Raman R; Sen P; Khan R; Giridhar A; Haridas S; Iglicki M; Zur D; Loewenstein A; Negri HP; Szeto S; Lam BKY; Tham CC; Sivaprasad S; Mckay M; Cheung CY
    Ophthalmol Retina; 2021 Nov; 5(11):1097-1106. PubMed ID: 33540169
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 57(13):5200-5206. PubMed ID: 27701631
    [TBL] [Abstract][Full Text] [Related]  

  • 9. First Incidence and Progression Study for Diabetic Retinopathy in Portugal, the RETINODIAB Study: Evaluation of the Screening Program for Lisbon Region.
    Dutra Medeiros M; Mesquita E; Gardete-Correia L; Moita J; Genro V; Papoila AL; Amaral-Turkman A; Raposo JF
    Ophthalmology; 2015 Dec; 122(12):2473-81. PubMed ID: 26383994
    [TBL] [Abstract][Full Text] [Related]  

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

  • 11. A novel device for accurate and efficient testing for vision-threatening diabetic retinopathy.
    Maa AY; Feuer WJ; Davis CQ; Pillow EK; Brown TD; Caywood RM; Chasan JE; Fransen SR
    J Diabetes Complications; 2016 Apr; 30(3):524-32. PubMed ID: 26803474
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
    Ting DSW; Cheung CY; Lim G; Tan GSW; Quang ND; Gan A; Hamzah H; Garcia-Franco R; San Yeo IY; Lee SY; Wong EYM; Sabanayagam C; Baskaran M; Ibrahim F; Tan NC; Finkelstein EA; Lamoureux EL; Wong IY; Bressler NM; Sivaprasad S; Varma R; Jonas JB; He MG; Cheng CY; Cheung GCM; Aung T; Hsu W; Lee ML; Wong TY
    JAMA; 2017 Dec; 318(22):2211-2223. PubMed ID: 29234807
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.
    Wang K; Jayadev C; Nittala MG; Velaga SB; Ramachandra CA; Bhaskaranand M; Bhat S; Solanki K; Sadda SR
    Acta Ophthalmol; 2018 Mar; 96(2):e168-e173. PubMed ID: 28926199
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Analysis of yield of retinal imaging in a rural diabetes eye care model.
    Rani PK; Bhattarai Y; Sheeladevi S; ShivaVaishnavi K; Ali MH; Babu JG
    Indian J Ophthalmol; 2018 Feb; 66(2):233-237. PubMed ID: 29380765
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy.
    Prathiba V; Rajalakshmi R; Arulmalar S; Usha M; Subhashini R; Gilbert CE; Anjana RM; Mohan V
    Indian J Ophthalmol; 2020 Feb; 68(Suppl 1):S42-S46. PubMed ID: 31937728
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep Learning-Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance.
    Nielsen KB; Lautrup ML; Andersen JKH; Savarimuthu TR; Grauslund J
    Ophthalmol Retina; 2019 Apr; 3(4):294-304. PubMed ID: 31014679
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study.
    Kubin AM; Wirkkala J; Keskitalo A; Ohtonen P; Hautala N
    Acta Ophthalmol; 2021 Dec; 99(8):e1415-e1420. PubMed ID: 33724706
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 39.