448 related articles for article (PubMed ID: 33539253)
1. Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs.
Gilbert MJ; Sun JK
Semin Ophthalmol; 2020 Nov; 35(7-8):325-332. PubMed ID: 33539253
[No Abstract] [Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
Wang XN; Dai L; Li ST; Kong HY; Sheng B; Wu Q
Curr Eye Res; 2020 Dec; 45(12):1550-1555. PubMed ID: 32410471
[No Abstract] [Full Text] [Related]
6. 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]
7. 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]
8. 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]
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. 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]
11. Artificial intelligence for diabetic retinopathy screening: a review.
Grzybowski A; Brona P; Lim G; Ruamviboonsuk P; Tan GSW; Abramoff M; Ting DSW
Eye (Lond); 2020 Mar; 34(3):451-460. PubMed ID: 31488886
[TBL] [Abstract][Full Text] [Related]
12. Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.
Raman R; Srinivasan S; Virmani S; Sivaprasad S; Rao C; Rajalakshmi R
Eye (Lond); 2019 Jan; 33(1):97-109. PubMed ID: 30401899
[TBL] [Abstract][Full Text] [Related]
13. Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.
Surya J; Garima ; Pandy N; Hyungtaek Rim T; Lee G; Priya MNS; Subramanian B; Raman R
Indian J Ophthalmol; 2023 Aug; 71(8):3039-3045. PubMed ID: 37530278
[TBL] [Abstract][Full Text] [Related]
14. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
Hua CH; Huynh-The T; Kim K; Yu SY; Le-Tien T; Park GH; Bang J; Khan WA; Bae SH; Lee S
Int J Med Inform; 2019 Dec; 132():103926. PubMed ID: 31605882
[TBL] [Abstract][Full Text] [Related]
15. The Role of Telemedicine, In-Home Testing and Artificial Intelligence to Alleviate an Increasingly Burdened Healthcare System: Diabetic Retinopathy.
Pieczynski J; Kuklo P; Grzybowski A
Ophthalmol Ther; 2021 Sep; 10(3):445-464. PubMed ID: 34156632
[TBL] [Abstract][Full Text] [Related]
16. Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.
Huang X; Wang H; She C; Feng J; Liu X; Hu X; Chen L; Tao Y
Front Endocrinol (Lausanne); 2022; 13():946915. PubMed ID: 36246896
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs.
Quellec G; Lamard M; Cazuguel G; Bekri L; Daccache W; Roux C; Cochener B
Invest Ophthalmol Vis Sci; 2011 Oct; 52(11):8342-8. PubMed ID: 21896872
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy.
Takahashi H; Tampo H; Arai Y; Inoue Y; Kawashima H
PLoS One; 2017; 12(6):e0179790. PubMed ID: 28640840
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]