370 related articles for article (PubMed ID: 34349130)
1. Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.
Scheetz J; Koca D; McGuinness M; Holloway E; Tan Z; Zhu Z; O'Day R; Sandhu S; MacIsaac RJ; Gilfillan C; Turner A; Keel S; He M
Sci Rep; 2021 Aug; 11(1):15808. PubMed ID: 34349130
[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. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study.
Keel S; Lee PY; Scheetz J; Li Z; Kotowicz MA; MacIsaac RJ; He M
Sci Rep; 2018 Mar; 8(1):4330. PubMed ID: 29531299
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda.
Whitestone N; Nkurikiye J; Patnaik JL; Jaccard N; Lanouette G; Cherwek DH; Congdon N; Mathenge W
Br J Ophthalmol; 2024 May; 108(6):840-845. PubMed ID: 37541766
[TBL] [Abstract][Full Text] [Related]
6. 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]
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. 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]
9. Prevalence of diabetic retinopathy and reduced vision among indigenous Australians in the nurse-led integrated Diabetes Education and Eye Screening study in a regional primary care clinic.
Atkinson-Briggs S; Jenkins A; Keech A; Ryan C; Brazionis L;
Intern Med J; 2023 Jul; 53(7):1188-1195. PubMed ID: 34779559
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment.
Skevas C; de Olaguer NP; Lleó A; Thiwa D; Schroeter U; Lopes IV; Mautone L; Linke SJ; Spitzer MS; Yap D; Xiao D
BMC Ophthalmol; 2024 Feb; 24(1):51. PubMed ID: 38302908
[TBL] [Abstract][Full Text] [Related]
12. Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy.
Ipp E; Liljenquist D; Bode B; Shah VN; Silverstein S; Regillo CD; Lim JI; Sadda S; Domalpally A; Gray G; Bhaskaranand M; Ramachandra C; Solanki K;
JAMA Netw Open; 2021 Nov; 4(11):e2134254. PubMed ID: 34779843
[TBL] [Abstract][Full Text] [Related]
13. Real-world evaluation of smartphone-based artificial intelligence to screen for diabetic retinopathy in Dominica: a clinical validation study.
Kemp O; Bascaran C; Cartwright E; McQuillan L; Matthew N; Shillingford-Ricketts H; Zondervan M; Foster A; Burton M
BMJ Open Ophthalmol; 2023 Dec; 8(1):. PubMed ID: 38135351
[TBL] [Abstract][Full Text] [Related]
14. [Using artificial intelligence as an initial triage strategy in diabetic retinopathy screening program in China].
Li ZX; Zhang J; Fong N; He MG
Zhonghua Yi Xue Za Zhi; 2020 Dec; 100(48):3835-3840. PubMed ID: 33371627
[No Abstract] [Full Text] [Related]
15. Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial.
Cleland CR; Bascaran C; Makupa W; Shilio B; Sandi FA; Philippin H; Marques AP; Egan C; Tufail A; Keane PA; Denniston AK; Macleod D; Burton MJ
BMJ Open; 2024 Jan; 14(1):e075055. PubMed ID: 38272554
[TBL] [Abstract][Full Text] [Related]
16. Screening for diabetic retinopathy and reduced vision among Indigenous Australians in Top End primary care health services: a TEAMSnet sub-study.
Quinn N; Yang F; Ryan C; Bursell SE; Keech A; Atkinson-Briggs S; Jenkins A; Brazionis L;
Intern Med J; 2021 Nov; 51(11):1897-1905. PubMed ID: 33196133
[TBL] [Abstract][Full Text] [Related]
17. What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting.
Willis K; Chaudhry UAR; Chandrasekaran L; Wahlich C; Olvera-Barrios A; Chambers R; Bolter L; Anderson J; Barman SA; Fajtl J; Welikala R; Egan C; Tufail A; Owen CG; Rudnicka A; ;
BMJ Open; 2023 Nov; 13(11):e075558. PubMed ID: 37968006
[TBL] [Abstract][Full Text] [Related]
18. The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes.
Bhaskaranand M; Ramachandra C; Bhat S; Cuadros J; Nittala MG; Sadda SR; Solanki K
Diabetes Technol Ther; 2019 Nov; 21(11):635-643. PubMed ID: 31335200
[No Abstract] [Full Text] [Related]
19. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders.
Tufail A; Rudisill C; Egan C; Kapetanakis VV; Salas-Vega S; Owen CG; Lee A; Louw V; Anderson J; Liew G; Bolter L; Srinivas S; Nittala M; Sadda S; Taylor P; Rudnicka AR
Ophthalmology; 2017 Mar; 124(3):343-351. PubMed ID: 28024825
[TBL] [Abstract][Full Text] [Related]
20. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness.
Tufail A; Kapetanakis VV; Salas-Vega S; Egan C; Rudisill C; Owen CG; Lee A; Louw V; Anderson J; Liew G; Bolter L; Bailey C; Sadda S; Taylor P; Rudnicka AR
Health Technol Assess; 2016 Dec; 20(92):1-72. PubMed ID: 27981917
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
