524 related articles for article (PubMed ID: 34759664)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
Shah P; Mishra DK; Shanmugam MP; Doshi B; Jayaraj H; Ramanjulu R
Indian J Ophthalmol; 2020 Feb; 68(2):398-405. PubMed ID: 31957737
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Automated diabetic retinopathy detection with two different retinal imaging devices using artificial intelligence: a comparison study.
Sarao V; Veritti D; Lanzetta P
Graefes Arch Clin Exp Ophthalmol; 2020 Dec; 258(12):2647-2654. PubMed ID: 32936359
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs.
Li Z; Keel S; Liu C; He Y; Meng W; Scheetz J; Lee PY; Shaw J; Ting D; Wong TY; Taylor H; Chang R; He M
Diabetes Care; 2018 Dec; 41(12):2509-2516. PubMed ID: 30275284
[TBL] [Abstract][Full Text] [Related]
14. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.
Hsieh YT; Chuang LM; Jiang YD; Chang TJ; Yang CM; Yang CH; Chan LW; Kao TY; Chen TC; Lin HC; Tsai CH; Chen M
J Formos Med Assoc; 2021 Jan; 120(1 Pt 1):165-171. PubMed ID: 32307321
[TBL] [Abstract][Full Text] [Related]
15. Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.
Grzybowski A; Rao DP; Brona P; Negiloni K; Krzywicki T; Savoy FM
Ophthalmic Res; 2023; 66(1):1286-1292. PubMed ID: 37757777
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population.
Shah A; Clarida W; Amelon R; Hernaez-Ortega MC; Navea A; Morales-Olivas J; Dolz-Marco R; Verbraak F; Jorda PP; van der Heijden AA; Peris Martinez C
J Diabetes Sci Technol; 2021 May; 15(3):655-663. PubMed ID: 32174153
[TBL] [Abstract][Full Text] [Related]
18. Simple, Mobile-based Artificial Intelligence Algo
Sosale B; Aravind SR; Murthy H; Narayana S; Sharma U; Gowda SGV; Naveenam M
BMJ Open Diabetes Res Care; 2020 Jan; 8(1):. PubMed ID: 32049632
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]