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 *

374 related articles for article (PubMed ID: 34349130)

  • 41. Convolutional Neural Networks Accurately Identify Ungradable Images in a Diabetic Retinopathy Telemedicine Screening Program.
    Bryan JM; Bryar PJ; Mirza RG
    Telemed J E Health; 2023 Sep; 29(9):1349-1355. PubMed ID: 36730708
    [No Abstract]   [Full Text] [Related]  

  • 42. Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China.
    Hao S; Liu C; Li N; Wu Y; Li D; Gao Q; Yuan Z; Li G; Li H; Yang J; Fan S
    PLoS One; 2022; 17(10):e0275983. PubMed ID: 36227905
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data.
    Lin S; Ma Y; Xu Y; Lu L; He J; Zhu J; Peng Y; Yu T; Congdon N; Zou H
    JMIR Public Health Surveill; 2023 Feb; 9():e41624. PubMed ID: 36821353
    [TBL] [Abstract][Full Text] [Related]  

  • 44. 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]  

  • 45. Artificial Intelligence Algorithms in Diabetic Retinopathy Screening.
    Zafar S; Mahjoub H; Mehta N; Domalpally A; Channa R
    Curr Diab Rep; 2022 Jun; 22(6):267-274. PubMed ID: 35438458
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases.
    Gu C; Wang Y; Jiang Y; Xu F; Wang S; Liu R; Yuan W; Abudureyimu N; Wang Y; Lu Y; Li X; Wu T; Dong L; Chen Y; Wang B; Zhang Y; Wei WB; Qiu Q; Zheng Z; Liu D; Chen J
    Br J Ophthalmol; 2024 Feb; 108(3):424-431. PubMed ID: 36878715
    [TBL] [Abstract][Full Text] [Related]  

  • 47. 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]  

  • 48. How Can Artificial Intelligence Be Implemented Effectively in Diabetic Retinopathy Screening in Japan?
    Kawasaki R
    Medicina (Kaunas); 2024 Jan; 60(2):. PubMed ID: 38399532
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China.
    Huang XM; Yang BF; Zheng WL; Liu Q; Xiao F; Ouyang PW; Li MJ; Li XY; Meng J; Zhang TT; Cui YH; Pan HW
    BMC Health Serv Res; 2022 Feb; 22(1):260. PubMed ID: 35216586
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care.
    Kanagasingam Y; Xiao D; Vignarajan J; Preetham A; Tay-Kearney ML; Mehrotra A
    JAMA Netw Open; 2018 Sep; 1(5):e182665. PubMed ID: 30646178
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients.
    Pei X; Yao X; Yang Y; Zhang H; Xia M; Huang R; Wang Y; Li Z
    Diabetes Res Clin Pract; 2022 Feb; 184():109190. PubMed ID: 35031348
    [TBL] [Abstract][Full Text] [Related]  

  • 52. 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]  

  • 53. Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.
    Surya J; Garima ; Pandy N; Hyungtaek Rim T; Lee G; Priya MNS; Subramanian B; Raman R
    Indian J Ophthalmol; 2023 Aug; 71(8):3039-3045. PubMed ID: 37530278
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial.
    Mathenge W; Whitestone N; Nkurikiye J; Patnaik JL; Piyasena P; Uwaliraye P; Lanouette G; Kahook MY; Cherwek DH; Congdon N; Jaccard N
    Ophthalmol Sci; 2022 Dec; 2(4):100168. PubMed ID: 36531575
    [TBL] [Abstract][Full Text] [Related]  

  • 55. A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System.
    Mokhashi N; Grachevskaya J; Cheng L; Yu D; Lu X; Zhang Y; Henderer JD
    J Diabetes Sci Technol; 2022 Jul; 16(4):1003-1007. PubMed ID: 33719599
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral.
    Korot E; Gonçalves MB; Huemer J; Beqiri S; Khalid H; Kelly M; Chia M; Mathijs E; Struyven R; Moussa M; Keane PA
    JAMA Ophthalmol; 2023 Nov; 141(11):1029-1036. PubMed ID: 37856110
    [TBL] [Abstract][Full Text] [Related]  

  • 57. A model of culturally-informed integrated diabetes education and eye screening in indigenous primary care services and specialist diabetes clinics: Study protocol.
    Atkinson-Briggs S; Jenkins A; Keech A; Ryan C; Brazionis L;
    J Adv Nurs; 2021 Mar; 77(3):1578-1590. PubMed ID: 33426727
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting.
    Lupidi M; Danieli L; Fruttini D; Nicolai M; Lassandro N; Chhablani J; Mariotti C
    Acta Diabetol; 2023 Aug; 60(8):1083-1088. PubMed ID: 37154944
    [TBL] [Abstract][Full Text] [Related]  

  • 59. A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence.
    Lalithadevi B; Krishnaveni S; Gnanadurai JSC
    J Med Syst; 2023 Aug; 47(1):85. PubMed ID: 37552340
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Screening for diabetic retinopathy with artificial intelligence: a real world evaluation.
    Burlina S; Radin S; Poggiato M; Cioccoloni D; Raimondo D; Romanello G; Tommasi C; Lombardi S
    Acta Diabetol; 2024 Jul; ():. PubMed ID: 38995312
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 19.