BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

580 related articles for article (PubMed ID: 31335200)

  • 1. 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]  

  • 2. 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]  

  • 3. EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events.
    Vought R; Vought V; Shah M; Szirth B; Bhagat N
    Int Ophthalmol; 2023 Dec; 43(12):4851-4859. PubMed ID: 37847478
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists' Dilated Examinations.
    Lim JI; Regillo CD; Sadda SR; Ipp E; Bhaskaranand M; Ramachandra C; Solanki K
    Ophthalmol Sci; 2023 Mar; 3(1):100228. PubMed ID: 36345378
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 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. 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]  

  • 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. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.
    Olvera-Barrios A; Heeren TF; Balaskas K; Chambers R; Bolter L; Egan C; Tufail A; Anderson J
    Br J Ophthalmol; 2021 Feb; 105(2):265-270. PubMed ID: 32376611
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.
    Ipp E; Liljenquist D; Bode B; Shah VN; Silverstein S; Regillo CD; Lim JI; Sadda S; Domalpally A; Gray G; Bhaskaranand M; Ramachandra C; Solanki K;
    JAMA Netw Open; 2021 Nov; 4(11):e2134254. PubMed ID: 34779843
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 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; 32(6):1138-1144. PubMed ID: 29520050
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. [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]  

  • 18. Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis.
    Wintergerst MWM; Bejan V; Hartmann V; Schnorrenberg M; Bleckwenn M; Weckbecker K; Finger RP
    Ophthalmic Epidemiol; 2022 Jun; 29(3):286-295. PubMed ID: 34151725
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 29.