BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

251 related articles for article (PubMed ID: 34779843)

  • 1. 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]  

  • 2. 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]  

  • 3. Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.
    Malerbi FK; Nakayama LF; Melo GB; Stuchi JA; Lencione D; Prado PV; Ribeiro LZ; Dib SA; Regatieri CV
    Ophthalmol Sci; 2024; 4(4):100481. PubMed ID: 38694494
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Evaluation of an Artificial Intelligence System for the Detection of Diabetic Retinopathy in Chinese Community Healthcare Centers.
    Dong X; Du S; Zheng W; Cai C; Liu H; Zou J
    Front Med (Lausanne); 2022; 9():883462. PubMed ID: 35479949
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparison of Handheld Retinal Imaging with ETDRS 7-Standard Field Photography for Diabetic Retinopathy and Diabetic Macular Edema.
    Salongcay RP; Aquino LAC; Salva CMG; Saunar AV; Alog GP; Sun JK; Peto T; Silva PS
    Ophthalmol Retina; 2022 Jul; 6(7):548-556. PubMed ID: 35278726
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. Multimodal imaging interpreted by graders to detect re-activation of diabetic eye disease in previously treated patients: the EMERALD diagnostic accuracy study.
    Lois N; Cook J; Wang A; Aldington S; Mistry H; Maredza M; McAuley D; Aslam T; Bailey C; Chong V; Ghanchi F; Scanlon P; Sivaprasad S; Steel D; Styles C; Azuara-Blanco A; Prior L; Waugh N
    Health Technol Assess; 2021 May; 25(32):1-104. PubMed ID: 34060440
    [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. 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]  

  • 13. Comparison of Early Treatment Diabetic Retinopathy Study Standard 7-Field Imaging With Ultrawide-Field Imaging for Determining Severity of Diabetic Retinopathy.
    Aiello LP; Odia I; Glassman AR; Melia M; Jampol LM; Bressler NM; Kiss S; Silva PS; Wykoff CC; Sun JK;
    JAMA Ophthalmol; 2019 Jan; 137(1):65-73. PubMed ID: 30347105
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Stereo nonmydriatic digital-video color retinal imaging compared with Early Treatment Diabetic Retinopathy Study seven standard field 35-mm stereo color photos for determining level of diabetic retinopathy.
    Bursell SE; Cavallerano JD; Cavallerano AA; Clermont AC; Birkmire-Peters D; Aiello LP; Aiello LM;
    Ophthalmology; 2001 Mar; 108(3):572-85. PubMed ID: 11237913
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth.
    Wolf RM; Liu TYA; Thomas C; Prichett L; Zimmer-Galler I; Smith K; Abramoff MD; Channa R
    Diabetes Care; 2021 Mar; 44(3):781-787. PubMed ID: 33479160
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy.
    Silva PS; Cavallerano JD; Sun JK; Noble J; Aiello LM; Aiello LP
    Am J Ophthalmol; 2012 Sep; 154(3):549-559.e2. PubMed ID: 22626617
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.