392 related articles for article (PubMed ID: 29548646)
1. Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.
Krause J; Gulshan V; Rahimy E; Karth P; Widner K; Corrado GS; Peng L; Webster DR
Ophthalmology; 2018 Aug; 125(8):1264-1272. PubMed ID: 29548646
[TBL] [Abstract][Full Text] [Related]
2. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.
Sayres R; Taly A; Rahimy E; Blumer K; Coz D; Hammel N; Krause J; Narayanaswamy A; Rastegar Z; Wu D; Xu S; Barb S; Joseph A; Shumski M; Smith J; Sood AB; Corrado GS; Peng L; Webster DR
Ophthalmology; 2019 Apr; 126(4):552-564. PubMed ID: 30553900
[TBL] [Abstract][Full Text] [Related]
3. Automated Identification of Diabetic Retinopathy Using Deep Learning.
Gargeya R; Leng T
Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. 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]
8. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.
Ting DSW; Cheung CY; Lim G; Tan GSW; Quang ND; Gan A; Hamzah H; Garcia-Franco R; San Yeo IY; Lee SY; Wong EYM; Sabanayagam C; Baskaran M; Ibrahim F; Tan NC; Finkelstein EA; Lamoureux EL; Wong IY; Bressler NM; Sivaprasad S; Varma R; Jonas JB; He MG; Cheng CY; Cheung GCM; Aung T; Hsu W; Lee ML; Wong TY
JAMA; 2017 Dec; 318(22):2211-2223. PubMed ID: 29234807
[TBL] [Abstract][Full Text] [Related]
9. Deep Learning-Based Algorithms in Screening of Diabetic Retinopathy: A Systematic Review of Diagnostic Performance.
Nielsen KB; Lautrup ML; Andersen JKH; Savarimuthu TR; Grauslund J
Ophthalmol Retina; 2019 Apr; 3(4):294-304. PubMed ID: 31014679
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Accuracy of trained rural ophthalmologists versus non-medical image graders in the diagnosis of diabetic retinopathy in rural China.
McKenna M; Chen T; McAneney H; Vázquez Membrillo MA; Jin L; Xiao W; Peto T; He M; Hogg R; Congdon N
Br J Ophthalmol; 2018 Nov; 102(11):1471-1476. PubMed ID: 29973366
[TBL] [Abstract][Full Text] [Related]
13. Automated analysis of retinal images for detection of referable diabetic retinopathy.
Abràmoff MD; Folk JC; Han DP; Walker JD; Williams DF; Russell SR; Massin P; Cochener B; Gain P; Tang L; Lamard M; Moga DC; Quellec G; Niemeijer M
JAMA Ophthalmol; 2013 Mar; 131(3):351-7. PubMed ID: 23494039
[TBL] [Abstract][Full Text] [Related]
14. Impact of targeted diabetic retinopathy training for graders in Vietnam and the implications for future diabetic retinopathy screening programmes: a diagnostic test accuracy study.
Curran K; Congdon N; Hoang TT; Lohfeld L; Nguyen VT; Nguyen HT; Nguyen QN; Dardis C; Virgili G; Piyasena P; Tran H; Salongcay RP; Tung MQ; Peto T
BMJ Open; 2022 Sep; 12(9):e059205. PubMed ID: 36691192
[TBL] [Abstract][Full Text] [Related]
15. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program.
Raumviboonsuk P; Krause J; Chotcomwongse P; Sayres R; Raman R; Widner K; Campana BJL; Phene S; Hemarat K; Tadarati M; Silpa-Archa S; Limwattanayingyong J; Rao C; Kuruvilla O; Jung J; Tan J; Orprayoon S; Kangwanwongpaisan C; Sukumalpaiboon R; Luengchaichawang C; Fuangkaew J; Kongsap P; Chualinpha L; Saree S; Kawinpanitan S; Mitvongsa K; Lawanasakol S; Thepchatri C; Wongpichedchai L; Corrado GS; Peng L; Webster DR
NPJ Digit Med; 2019; 2():25. PubMed ID: 31304372
[TBL] [Abstract][Full Text] [Related]
16. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.
Islam MM; Yang HC; Poly TN; Jian WS; Jack Li YC
Comput Methods Programs Biomed; 2020 Jul; 191():105320. PubMed ID: 32088490
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Inter-grader reliability in the Danish screening programme for diabetic retinopathy.
Thykjaer AS; Andresen J; Andersen N; Bek T; Heegaard S; Hajari J; Schmidt Laugesen C; Möller S; Pedersen FN; Kawasaki R; Højlund K; Rubin KH; Stokholm L; Peto T; Grauslund J
Acta Ophthalmol; 2023 Nov; 101(7):783-788. PubMed ID: 37066883
[TBL] [Abstract][Full Text] [Related]
20. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India.
Gulshan V; Rajan RP; Widner K; Wu D; Wubbels P; Rhodes T; Whitehouse K; Coram M; Corrado G; Ramasamy K; Raman R; Peng L; Webster DR
JAMA Ophthalmol; 2019 Sep; 137(9):987-993. PubMed ID: 31194246
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
