514 related articles for article (PubMed ID: 32631221)
1. Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning.
Karakaya M; Hacisoftaoglu RE
BMC Bioinformatics; 2020 Jul; 21(Suppl 4):259. PubMed ID: 32631221
[TBL] [Abstract][Full Text] [Related]
2. Deep Learning Frameworks for Diabetic Retinopathy Detection with Smartphone-based Retinal Imaging Systems.
Hacisoftaoglu RE; Karakaya M; Sallam AB
Pattern Recognit Lett; 2020 Jul; 135():409-417. PubMed ID: 32704196
[TBL] [Abstract][Full Text] [Related]
3. Clinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study.
Naz H; Nijhawan R; Ahuja NJ
Int Ophthalmol; 2024 Feb; 44(1):41. PubMed ID: 38334896
[TBL] [Abstract][Full Text] [Related]
4. Diabetic Retinopathy Screening Using Smartphone-Based Fundus Imaging in India.
Wintergerst MWM; Mishra DK; Hartmann L; Shah P; Konana VK; Sagar P; Berger M; Murali K; Holz FG; Shanmugam MP; Finger RP
Ophthalmology; 2020 Nov; 127(11):1529-1538. PubMed ID: 32464129
[TBL] [Abstract][Full Text] [Related]
5. Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy.
Prathiba V; Rajalakshmi R; Arulmalar S; Usha M; Subhashini R; Gilbert CE; Anjana RM; Mohan V
Indian J Ophthalmol; 2020 Feb; 68(Suppl 1):S42-S46. PubMed ID: 31937728
[TBL] [Abstract][Full Text] [Related]
6. Validity of smartphone-based retinal photography (PEEK-retina) compared to the standard ophthalmic fundus camera in diagnosing diabetic retinopathy in Uganda: A cross-sectional study.
Yusuf AM; Lusobya RC; Mukisa J; Batte C; Nakanjako D; Juliet-Sengeri O
PLoS One; 2022; 17(9):e0273633. PubMed ID: 36067194
[TBL] [Abstract][Full Text] [Related]
7. Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection.
Gupta S; Thakur S; Gupta A
Multimed Tools Appl; 2022; 81(10):14475-14501. PubMed ID: 35233182
[TBL] [Abstract][Full Text] [Related]
8. Validation of Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening.
Bilong Y; Katte JC; Koki G; Kagmeni G; Obama OPN; Fofe HRN; Mvilongo C; Nkengfack O; Bimbai AM; Sobngwi E; Mbacham W; Mbanya JC; Bella LA; Sharma A
Ophthalmic Surg Lasers Imaging Retina; 2019 May; 50(5):S18-S22. PubMed ID: 31100178
[TBL] [Abstract][Full Text] [Related]
9. Review of retinal cameras for global coverage of diabetic retinopathy screening.
Rajalakshmi R; Prathiba V; Arulmalar S; Usha M
Eye (Lond); 2021 Jan; 35(1):162-172. PubMed ID: 33168977
[TBL] [Abstract][Full Text] [Related]
10. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
Asiri N; Hussain M; Al Adel F; Alzaidi N
Artif Intell Med; 2019 Aug; 99():101701. PubMed ID: 31606116
[TBL] [Abstract][Full Text] [Related]
11. Sensitivity and Specificity of Smartphone-Based Retinal Imaging for Diabetic Retinopathy: A Comparative Study.
Sengupta S; Sindal MD; Baskaran P; Pan U; Venkatesh R
Ophthalmol Retina; 2019 Feb; 3(2):146-153. PubMed ID: 31014763
[TBL] [Abstract][Full Text] [Related]
12. The feasibility of smartphone based retinal photography for diabetic retinopathy screening among Brazilian Xavante Indians.
Korn Malerbi F; Lelis Dal Fabbro A; Botelho Vieira Filho JP; Franco LJ
Diabetes Res Clin Pract; 2020 Oct; 168():108380. PubMed ID: 32828834
[TBL] [Abstract][Full Text] [Related]
13. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
[TBL] [Abstract][Full Text] [Related]
14. Current status and future possibilities of retinal imaging in diabetic retinopathy care applicable to low- and medium-income countries.
Attiku Y; He Y; Nittala MG; Sadda SR
Indian J Ophthalmol; 2021 Nov; 69(11):2968-2976. PubMed ID: 34708731
[TBL] [Abstract][Full Text] [Related]
15. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.
Xu K; Feng D; Mi H
Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750
[TBL] [Abstract][Full Text] [Related]
16. Diabetic retinopathy screening in urban primary care setting with a handheld smartphone-based retinal camera.
Queiroz MS; de Carvalho JX; Bortoto SF; de Matos MR; das Graças Dias Cavalcante C; Andrade EAS; Correa-Giannella ML; Malerbi FK
Acta Diabetol; 2020 Dec; 57(12):1493-1499. PubMed ID: 32748176
[TBL] [Abstract][Full Text] [Related]
17. 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]
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. 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]
20. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
Wei H; Shi P; Miao J; Zhang M; Bai G; Qiu J; Liu F; Yuan W
Comput Biol Med; 2024 Jun; 175():108459. PubMed ID: 38701588
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
