BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

204 related articles for article (PubMed ID: 37757777)

  • 1. 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]  

  • 2. 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]  

  • 3. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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; 21(11):635-643. PubMed ID: 31335200
    [No Abstract]   [Full Text] [Related]  

  • 7. Simple, Mobile-based Artificial Intelligence Algo
    Sosale B; Aravind SR; Murthy H; Narayana S; Sharma U; Gowda SGV; Naveenam M
    BMJ Open Diabetes Res Care; 2020 Jan; 8(1):. PubMed ID: 32049632
    [TBL] [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; 56(1):2352018. PubMed ID: 38738798
    [TBL] [Abstract][Full Text] [Related]  

  • 9. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness.
    Tufail A; Kapetanakis VV; Salas-Vega S; Egan C; Rudisill C; Owen CG; Lee A; Louw V; Anderson J; Liew G; Bolter L; Bailey C; Sadda S; Taylor P; Rudnicka AR
    Health Technol Assess; 2016 Dec; 20(92):1-72. PubMed ID: 27981917
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Use of artificial intelligence in screening for diabetic retinopathy at a tertiary diabetes center].
    Paul S; Tayar A; Morawiec-Kisiel E; Bohl B; Großjohann R; Hunfeld E; Busch M; Pfeil JM; Dähmcke M; Brauckmann T; Eilts S; Bründer MC; Grundel M; Grundel B; Tost F; Kuhn J; Reindel J; Kerner W; Stahl A
    Ophthalmologie; 2022 Jul; 119(7):705-713. PubMed ID: 35080640
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [Using artificial intelligence as an initial triage strategy in diabetic retinopathy screening program in China].
    Li ZX; Zhang J; Fong N; He MG
    Zhonghua Yi Xue Za Zhi; 2020 Dec; 100(48):3835-3840. PubMed ID: 33371627
    [No Abstract]   [Full Text] [Related]  

  • 12. Towards a Device Agnostic AI for Diabetic Retinopathy Screening: An External Validation Study.
    Rao DP; Sindal MD; Sengupta S; Baskaran P; Venkatesh R; Sivaraman A; Savoy FM
    Clin Ophthalmol; 2022; 16():2659-2667. PubMed ID: 36003071
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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; 28(2):81-86. PubMed ID: 34759664
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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; 105(5):723-728. PubMed ID: 32606081
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Accuracy of Integrated Artificial Intelligence Grading Using Handheld Retinal Imaging in a Community Diabetic Eye Screening Program.
    Salongcay RP; Aquino LAC; Alog GP; Locaylocay KB; Saunar AV; Peto T; Silva PS
    Ophthalmol Sci; 2024; 4(3):100457. PubMed ID: 38317871
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence.
    Doğan ME; Bilgin AB; Sari R; Bulut M; Akar Y; Aydemir M
    Eye (Lond); 2024 Mar; ():. PubMed ID: 38467864
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 11.