BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

186 related articles for article (PubMed ID: 37856110)

  • 1. 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]  

  • 2. 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]  

  • 3. Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images.
    Jacoba CMP; Doan D; Salongcay RP; Aquino LAC; Silva JPY; Salva CMG; Zhang D; Alog GP; Zhang K; Locaylocay KLRB; Saunar AV; Ashraf M; Sun JK; Peto T; Aiello LP; Silva PS
    Ophthalmol Retina; 2023 Aug; 7(8):703-712. PubMed ID: 36924893
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.
    Burlina P; Paul W; Mathew P; Joshi N; Pacheco KD; Bressler NM
    JAMA Ophthalmol; 2020 Oct; 138(10):1070-1077. PubMed ID: 32880609
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 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. Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.
    Burlina P; Paul W; Liu TYA; Bressler NM
    JAMA Ophthalmol; 2022 Feb; 140(2):185-189. PubMed ID: 34967890
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.
    Zhang G; Lin JW; Wang J; Ji J; Cen LP; Chen W; Xie P; Zheng Y; Xiong Y; Wu H; Li D; Ng TK; Pang CP; Zhang M
    BMJ Open; 2022 Jul; 12(7):e060155. PubMed ID: 35902186
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.
    Stevenson CH; Hong SC; Ogbuehi KC
    Clin Exp Ophthalmol; 2019 May; 47(4):484-489. PubMed ID: 30370587
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.
    Lam C; Wong YL; Tang Z; Hu X; Nguyen TX; Yang D; Zhang S; Ding J; Szeto SKH; Ran AR; Cheung CY
    Diabetes Care; 2024 Feb; 47(2):304-319. PubMed ID: 38241500
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
    Voets M; Møllersen K; Bongo LA
    PLoS One; 2019; 14(6):e0217541. PubMed ID: 31170223
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data.
    Lo J; Yu TT; Ma D; Zang P; Owen JP; Zhang Q; Wang RK; Beg MF; Lee AY; Jia Y; Sarunic MV
    Ophthalmol Sci; 2021 Dec; 1(4):100069. PubMed ID: 36246944
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.
    Malerbi FK; Nakayama LF; Melo GB; Stuchi JA; Lencione D; Prado PV; Ribeiro LZ; Dib SA; Regatieri CV
    Ophthalmol Sci; 2024; 4(4):100481. PubMed ID: 38694494
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening.
    de Oliveira JAE; Nakayama LF; Zago Ribeiro L; de Oliveira TVF; Choi SNJH; Neto EM; Cardoso VS; Dib SA; Melo GB; Regatieri CVS; Malerbi FK
    Acta Diabetol; 2023 Aug; 60(8):1075-1081. PubMed ID: 37149834
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.
    Wu JT; Wong KCL; Gur Y; Ansari N; Karargyris A; Sharma A; Morris M; Saboury B; Ahmad H; Boyko O; Syed A; Jadhav A; Wang H; Pillai A; Kashyap S; Moradi M; Syeda-Mahmood T
    JAMA Netw Open; 2020 Oct; 3(10):e2022779. PubMed ID: 33034642
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.