562 related articles for article (PubMed ID: 32606081)
1. 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]
2. 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]
3. Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images.
Olvera-Barrios A; Heeren TF; Balaskas K; Chambers R; Bolter L; Egan C; Tufail A; Anderson J
Br J Ophthalmol; 2021 Feb; 105(2):265-270. PubMed ID: 32376611
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.
Vaghefi E; Yang S; Xie L; Hill S; Schmiedel O; Murphy R; Squirrell D
Diabet Med; 2021 Apr; 38(4):e14386. PubMed ID: 32794618
[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. 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]
8. 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]
9. 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]
10. A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme.
Kapetanakis VV; Rudnicka AR; Liew G; Owen CG; Lee A; Louw V; Bolter L; Anderson J; Egan C; Salas-Vega S; Rudisill C; Taylor P; Tufail A
J Med Screen; 2015 Sep; 22(3):112-8. PubMed ID: 25742804
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. Accuracy and reliability of retinal photo grading for diabetic retinopathy: Remote graders from a developing country and standard retinal photo grader in Australia.
Islam FMA
PLoS One; 2017; 12(6):e0179310. PubMed ID: 28632764
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. 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]
20. A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy.
Olson JA; Strachan FM; Hipwell JH; Goatman KA; McHardy KC; Forrester JV; Sharp PF
Diabet Med; 2003 Jul; 20(7):528-34. PubMed ID: 12823232
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
