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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

639 related items for PubMed ID: 38885761

  • 1. Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.
    Farahat Z, Zrira N, Souissi N, Bennani Y, Bencherif S, Benamar S, Belmekki M, Ngote MN, Megdiche K.
    Surv Ophthalmol; 2024; 69(5):707-721. PubMed ID: 38885761
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  • 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
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  • 3. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
    Hassan D, Gill HM, Happe M, Bhatwadekar AD, Hajrasouliha AR, Janga SC.
    Front Med (Lausanne); 2022 Feb; 9():1050436. PubMed ID: 36425113
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  • 4. 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
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  • 5. How Can Artificial Intelligence Be Implemented Effectively in Diabetic Retinopathy Screening in Japan?
    Kawasaki R.
    Medicina (Kaunas); 2024 Jan 30; 60(2):. PubMed ID: 38399532
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  • 12. 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 30; 1(1):e35-e44. PubMed ID: 33323239
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  • 13. 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 30; 34(3):451-460. PubMed ID: 31488886
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  • 14. 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 20; 21(1):47. PubMed ID: 35859144
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  • 17. 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 20; 191():105320. PubMed ID: 32088490
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  • 18. Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.
    Wang Y, Han X, Li C, Luo L, Yin Q, Zhang J, Peng G, Shi D, He M.
    J Med Internet Res; 2024 Aug 14; 26():e52506. PubMed ID: 39141915
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  • 19. Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment.
    Grzybowski A, Singhanetr P, Nanegrungsunk O, Ruamviboonsuk P.
    Ophthalmol Ther; 2023 Jun 14; 12(3):1419-1437. PubMed ID: 36862308
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  • 20. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
    Romero-Oraá R, Herrero-Tudela M, López MI, Hornero R, García M.
    Comput Methods Programs Biomed; 2024 Jun 14; 249():108160. PubMed ID: 38583290
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