BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

190 related articles for article (PubMed ID: 37757777)

  • 21. Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.
    Sedova A; Hajdu D; Datlinger F; Steiner I; Neschi M; Aschauer J; Gerendas BS; Schmidt-Erfurth U; Pollreisz A
    Eye (Lond); 2022 Mar; 36(3):510-516. PubMed ID: 35132211
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.
    Jacoba CMP; Doan D; Salongcay RP; Aquino LAC; Silva JPY; Salva CMG; Zhang D; Alog GP; Zhang K; Locaylocay KLRB; Saunar AV; Ashraf M; Sun JK; Peto T; Aiello LP; Silva PS
    Ophthalmol Retina; 2023 Aug; 7(8):703-712. PubMed ID: 36924893
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy.
    Jain A; Krishnan R; Rogye A; Natarajan S
    Indian J Ophthalmol; 2021 Nov; 69(11):3150-3154. PubMed ID: 34708760
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone.
    Natarajan S; Jain A; Krishnan R; Rogye A; Sivaprasad S
    JAMA Ophthalmol; 2019 Oct; 137(10):1182-1188. PubMed ID: 31393538
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. Automated feature-based grading and progression analysis of diabetic retinopathy.
    Al-Turk L; Wawrzynski J; Wang S; Krause P; Saleh GM; Alsawadi H; Alshamrani AZ; Peto T; Bastawrous A; Li J; Tang HL
    Eye (Lond); 2022 Mar; 36(3):524-532. PubMed ID: 33731888
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Feasibility and accuracy of the screening for diabetic retinopathy using a fundus camera and an artificial intelligence pre-evaluation application.
    Piatti A; Romeo F; Manti R; Doglio M; Tartaglino B; Nada E; Giorda CB
    Acta Diabetol; 2024 Jan; 61(1):63-68. PubMed ID: 37676288
    [TBL] [Abstract][Full Text] [Related]  

  • 28. 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]  

  • 29. Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread.
    Mehra AA; Softing A; Guner MK; Hodge DO; Barkmeier AJ
    Am J Ophthalmol; 2022 Dec; 244():125-132. PubMed ID: 35970206
    [TBL] [Abstract][Full Text] [Related]  

  • 30. THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.
    Vaghefi E; Yang S; Xie L; Hill S; Schmiedel O; Murphy R; Squirrell D
    Diabet Med; 2021 Apr; 38(4):e14386. PubMed ID: 32794618
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Artificial intelligence-based screening for diabetic retinopathy at community hospital.
    He J; Cao T; Xu F; Wang S; Tao H; Wu T; Sun L; Chen J
    Eye (Lond); 2020 Mar; 34(3):572-576. PubMed ID: 31455902
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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]  

  • 33. Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.
    Musetti D; Cutolo CA; Bonetto M; Giacomini M; Maggi D; Viviani GL; Gandin I; Traverso CE; Nicolò M
    Eur J Ophthalmol; 2024 Apr; ():11206721241248856. PubMed ID: 38656241
    [No Abstract]   [Full Text] [Related]  

  • 34. 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]  

  • 35. 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]  

  • 36. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda.
    Whitestone N; Nkurikiye J; Patnaik JL; Jaccard N; Lanouette G; Cherwek DH; Congdon N; Mathenge W
    Br J Ophthalmol; 2024 May; 108(6):840-845. PubMed ID: 37541766
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze.
    Grzybowski A; Brona P
    J Clin Med; 2021 May; 10(11):. PubMed ID: 34071990
    [TBL] [Abstract][Full Text] [Related]  

  • 39. 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]  

  • 40. The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.
    Qian X; Jingying H; Xian S; Yuqing Z; Lili W; Baorui C; Wei G; Yefeng Z; Qiang Z; Chunyan C; Cheng B; Kai M; Yi Q
    Front Public Health; 2022; 10():1025271. PubMed ID: 36419999
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.