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.
120 related articles for article (PubMed ID: 39134384)
21. 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]
22. Deep Capillary Geometric Perfusion Deficits on OCT Angiography Detect Clinically Referable Eyes with Diabetic Retinopathy. Nesper PL; Ong JX; Fawzi AA Ophthalmol Retina; 2022 Dec; 6(12):1194-1205. PubMed ID: 35661804 [TBL] [Abstract][Full Text] [Related]
23. 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]
24. Automated diabetic retinopathy screening for primary care settings using deep learning. Bhuiyan A; Govindaiah A; Deobhakta A; Hossain M; Rosen R; Smith T Intell Based Med; 2021; 5():. PubMed ID: 35528965 [TBL] [Abstract][Full Text] [Related]
25. 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]
26. 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]
27. 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]
28. Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning. Burlina P; Paul W; Liu TYA; Bressler NM JAMA Ophthalmol; 2022 Feb; 140(2):185-189. PubMed ID: 34967890 [TBL] [Abstract][Full Text] [Related]
29. Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening. de Oliveira JAE; Nakayama LF; Zago Ribeiro L; de Oliveira TVF; Choi SNJH; Neto EM; Cardoso VS; Dib SA; Melo GB; Regatieri CVS; Malerbi FK Acta Diabetol; 2023 Aug; 60(8):1075-1081. PubMed ID: 37149834 [TBL] [Abstract][Full Text] [Related]
30. Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy. Goh JHL; Ang E; Srinivasan S; Lei X; Loh J; Quek TC; Xue C; Xu X; Liu Y; Cheng CY; Rajapakse JC; Tham YC Ophthalmol Sci; 2024; 4(6):100552. PubMed ID: 39165694 [TBL] [Abstract][Full Text] [Related]
31. 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]
32. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. van der Heijden AA; Abramoff MD; Verbraak F; van Hecke MV; Liem A; Nijpels G Acta Ophthalmol; 2018 Feb; 96(1):63-68. PubMed ID: 29178249 [TBL] [Abstract][Full Text] [Related]
33. 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]
34. Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. Burlina PM; Joshi N; Pekala M; Pacheco KD; Freund DE; Bressler NM JAMA Ophthalmol; 2017 Nov; 135(11):1170-1176. PubMed ID: 28973096 [TBL] [Abstract][Full Text] [Related]
35. Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach. Wang Y; Shi D; Tan Z; Niu Y; Jiang Y; Xiong R; Peng G; He M Front Med (Lausanne); 2021; 8():740987. PubMed ID: 34901058 [No Abstract] [Full Text] [Related]
37. Optical coherence tomography for age-related macular degeneration and diabetic macular edema: an evidence-based analysis. Medical Advisory Secretariat Ont Health Technol Assess Ser; 2009; 9(13):1-22. PubMed ID: 23074517 [TBL] [Abstract][Full Text] [Related]
38. Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Li F; Wang Y; Xu T; Dong L; Yan L; Jiang M; Zhang X; Jiang H; Wu Z; Zou H Eye (Lond); 2022 Jul; 36(7):1433-1441. PubMed ID: 34211137 [TBL] [Abstract][Full Text] [Related]
39. Deep Capillary Nonperfusion on OCT Angiography Predicts Complications in Eyes with Referable Nonproliferative Diabetic Retinopathy. Ong JX; Konopek N; Fukuyama H; Fawzi AA Ophthalmol Retina; 2023 Jan; 7(1):14-23. PubMed ID: 35803524 [TBL] [Abstract][Full Text] [Related]
40. A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study. Li N; Ma M; Lai M; Gu L; Kang M; Wang Z; Jiao S; Dang K; Deng J; Ding X; Zhen Q; Zhang A; Shen T; Zheng Z; Wang Y; Peng Y J Diabetes; 2022 Feb; 14(2):111-120. PubMed ID: 34889059 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]