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.
186 related articles for article (PubMed ID: 39127709)
1. Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study. Nabrdalik K; Irlik K; Meng Y; Kwiendacz H; Piaśnik J; Hendel M; Ignacy P; Kulpa J; Kegler K; Herba M; Boczek S; Hashim EB; Gao Z; Gumprecht J; Zheng Y; Lip GYH; Alam U Cardiovasc Diabetol; 2024 Aug; 23(1):296. PubMed ID: 39127709 [TBL] [Abstract][Full Text] [Related]
2. Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes. Irlik K; Aldosari H; Hendel M; Kwiendacz H; Piaśnik J; Kulpa J; Ignacy P; Boczek S; Herba M; Kegler K; Coenen F; Gumprecht J; Zheng Y; Lip GYH; Alam U; Nabrdalik K Diabetes Obes Metab; 2024 Jul; 26(7):2624-2633. PubMed ID: 38603589 [TBL] [Abstract][Full Text] [Related]
3. 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]
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. Evaluation of AI-enhanced non-mydriatic fundus photography for diabetic retinopathy screening. Hu CL; Wang YC; Wu WF; Xi Y Photodiagnosis Photodyn Ther; 2024 Oct; 49():104331. PubMed ID: 39245303 [TBL] [Abstract][Full Text] [Related]
6. Can coefficient of variation of time-domain analysis be valuable for detecting cardiovascular autonomic neuropathy in young patients with type 1 diabetes: a case control study. Razanskaite-Virbickiene D; Danyte E; Mockeviciene G; Dobrovolskiene R; Verkauskiene R; Zalinkevicius R BMC Cardiovasc Disord; 2017 Jan; 17(1):34. PubMed ID: 28103812 [TBL] [Abstract][Full Text] [Related]
7. Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence. Doğan ME; Bilgin AB; Sari R; Bulut M; Akar Y; Aydemir M Eye (Lond); 2024 Jun; 38(9):1694-1701. PubMed ID: 38467864 [TBL] [Abstract][Full Text] [Related]
8. Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images. Stevenson CH; Hong SC; Ogbuehi KC Clin Exp Ophthalmol; 2019 May; 47(4):484-489. PubMed ID: 30370587 [TBL] [Abstract][Full Text] [Related]
9. Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images. Son J; Shin JY; Kim HD; Jung KH; Park KH; Park SJ Ophthalmology; 2020 Jan; 127(1):85-94. PubMed ID: 31281057 [TBL] [Abstract][Full Text] [Related]
10. Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases. Burlina P; Paul W; Mathew P; Joshi N; Pacheco KD; Bressler NM JAMA Ophthalmol; 2020 Oct; 138(10):1070-1077. PubMed ID: 32880609 [TBL] [Abstract][Full Text] [Related]
11. Artificial Intelligence-Assisted Early Detection of Retinitis Pigmentosa - the Most Common Inherited Retinal Degeneration. Chen TC; Lim WS; Wang VY; Ko ML; Chiu SI; Huang YS; Lai F; Yang CM; Hu FR; Jang JR; Yang CH J Digit Imaging; 2021 Aug; 34(4):948-958. PubMed ID: 34244880 [TBL] [Abstract][Full Text] [Related]
12. A cardiac risk score based on sudomotor function to evaluate cardiovascular autonomic neuropathy in asymptomatic Chinese patients with diabetes mellitus. Yuan T; Li J; Fu Y; Xu T; Li J; Wang X; Zhou Y; Dong Y; Zhao W PLoS One; 2018; 13(10):e0204804. PubMed ID: 30281621 [TBL] [Abstract][Full Text] [Related]
13. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976 [TBL] [Abstract][Full Text] [Related]
14. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Niemeijer M; van Ginneken B; Russell SR; Suttorp-Schulten MS; Abràmoff MD Invest Ophthalmol Vis Sci; 2007 May; 48(5):2260-7. PubMed ID: 17460289 [TBL] [Abstract][Full Text] [Related]
15. Assessment of Clinical Metadata on the Accuracy of Retinal Fundus Image Labels in Diabetic Retinopathy in Uganda: Case-Crossover Study Using the Multimodal Database of Retinal Images in Africa. Arunga S; Morley KE; Kwaga T; Morley MG; Nakayama LF; Mwavu R; Kaggwa F; Ssempiira J; Celi LA; Haberer JE; Obua C JMIR Form Res; 2024 Sep; 8():e59914. PubMed ID: 39293049 [TBL] [Abstract][Full Text] [Related]
16. Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya. White T; Selvarajah V; Wolfhagen-Sand F; Svangård N; Mohankumar G; Fenici P; Rough K; Onyango N; Lyons K; Mack C; Nduba V; Noorali Saleh M; Abayo I; Siddiqui A; Majdanska-Strzalka M; Kaszubska K; Hegelund-Myrback T; Esterline R; Manzur A; Parker VER Diabetes Obes Metab; 2024 Jul; 26(7):2722-2731. PubMed ID: 38618987 [TBL] [Abstract][Full Text] [Related]
17. Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. Liu H; Li L; Wormstone IM; Qiao C; Zhang C; Liu P; Li S; Wang H; Mou D; Pang R; Yang D; Zangwill LM; Moghimi S; Hou H; Bowd C; Jiang L; Chen Y; Hu M; Xu Y; Kang H; Ji X; Chang R; Tham C; Cheung C; Ting DSW; Wong TY; Wang Z; Weinreb RN; Xu M; Wang N JAMA Ophthalmol; 2019 Dec; 137(12):1353-1360. PubMed ID: 31513266 [TBL] [Abstract][Full Text] [Related]
18. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lin D; Xiong J; Liu C; Zhao L; Li Z; Yu S; Wu X; Ge Z; Hu X; Wang B; Fu M; Zhao X; Wang X; Zhu Y; Chen C; Li T; Li Y; Wei W; Zhao M; Li J; Xu F; Ding L; Tan G; Xiang Y; Hu Y; Zhang P; Han Y; Li JO; Wei L; Zhu P; Liu Y; Chen W; Ting DSW; Wong TY; Chen Y; Lin H Lancet Digit Health; 2021 Aug; 3(8):e486-e495. PubMed ID: 34325853 [TBL] [Abstract][Full Text] [Related]
19. Impaired hypoxic ventilatory drive induced by diabetic autonomic neuropathy, a cause of misdiagnosed severe cardiac events: brief report of two cases. Schubert L; Laroche S; Hartemann A; Bourron O; Phan F BMC Cardiovasc Disord; 2021 Mar; 21(1):140. PubMed ID: 33731006 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]