167 related articles for article (PubMed ID: 38135351)
1. 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]
2. 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]
3. 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]
4. 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]
5. Multimodal imaging interpreted by graders to detect re-activation of diabetic eye disease in previously treated patients: the EMERALD diagnostic accuracy study.
Lois N; Cook J; Wang A; Aldington S; Mistry H; Maredza M; McAuley D; Aslam T; Bailey C; Chong V; Ghanchi F; Scanlon P; Sivaprasad S; Steel D; Styles C; Azuara-Blanco A; Prior L; Waugh N
Health Technol Assess; 2021 May; 25(32):1-104. PubMed ID: 34060440
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone.
Natarajan S; Jain A; Krishnan R; Rogye A; Sivaprasad S
JAMA Ophthalmol; 2019 Oct; 137(10):1182-1188. PubMed ID: 31393538
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. 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]
12. Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis.
Hasan SU; Siddiqui MAR
Diabetes Res Clin Pract; 2023 Nov; 205():110943. PubMed ID: 37805002
[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. 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]
15. Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy.
Jain A; Krishnan R; Rogye A; Natarajan S
Indian J Ophthalmol; 2021 Nov; 69(11):3150-3154. PubMed ID: 34708760
[TBL] [Abstract][Full Text] [Related]
16. Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral.
Korot E; Gonçalves MB; Huemer J; Beqiri S; Khalid H; Kelly M; Chia M; Mathijs E; Struyven R; Moussa M; Keane PA
JAMA Ophthalmol; 2023 Nov; 141(11):1029-1036. PubMed ID: 37856110
[TBL] [Abstract][Full Text] [Related]
17. Validation of diagnostic accuracy of retinal image grading by trained non-ophthalmologist grader for detecting diabetic retinopathy and diabetic macular edema.
Joseph S; Rajan RP; Sundar B; Venkatachalam S; Kempen JH; Kim R
Eye (Lond); 2023 Jun; 37(8):1577-1582. PubMed ID: 35906419
[TBL] [Abstract][Full Text] [Related]
18. Artificial intelligence-based screening for diabetic retinopathy at community hospital.
He J; Cao T; Xu F; Wang S; Tao H; Wu T; Sun L; Chen J
Eye (Lond); 2020 Mar; 34(3):572-576. PubMed ID: 31455902
[TBL] [Abstract][Full Text] [Related]
19. 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]
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]