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.
127 related articles for article (PubMed ID: 38995312)
1. 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]
2. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. Diagnostic test accuracy of diabetic retinopathy screening by physician graders using a hand-held non-mydriatic retinal camera at a tertiary level medical clinic. Piyasena MMPN; Yip JLY; MacLeod D; Kim M; Gudlavalleti VSM BMC Ophthalmol; 2019 Apr; 19(1):89. PubMed ID: 30961576 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. 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]
13. A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening. Ruan S; Liu Y; Hu WT; Jia HX; Wang SS; Song ML; Shen MX; Luo DW; Ye T; Wang FH Int J Ophthalmol; 2022; 15(4):620-627. PubMed ID: 35450182 [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. Validation of diagnostic accuracy of retinal image grading by trained non-ophthalmologist grader for detecting diabetic retinopathy and diabetic macular edema. Joseph S; Rajan RP; Sundar B; Venkatachalam S; Kempen JH; Kim R Eye (Lond); 2023 Jun; 37(8):1577-1582. PubMed ID: 35906419 [TBL] [Abstract][Full Text] [Related]
16. 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]
17. Effectiveness and safety of screening for diabetic retinopathy with two nonmydriatic digital images compared with the seven standard stereoscopic photographic fields. Boucher MC; Gresset JA; Angioi K; Olivier S Can J Ophthalmol; 2003 Dec; 38(7):557-68. PubMed ID: 14740797 [TBL] [Abstract][Full Text] [Related]
19. 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]
20. Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera. Malerbi FK; Andrade RE; Morales PH; Stuchi JA; Lencione D; de Paulo JV; Carvalho MP; Nunes FS; Rocha RM; Ferraz DA; Belfort R J Diabetes Sci Technol; 2022 May; 16(3):716-723. PubMed ID: 33435711 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]