These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

291 related articles for article (PubMed ID: 35141420)

  • 1. Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.
    Abitbol E; Miere A; Excoffier JB; Mehanna CJ; Amoroso F; Kerr S; Ortala M; Souied EH
    BMJ Open Ophthalmol; 2022; 7(1):e000924. PubMed ID: 35141420
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ETDRS grading with CLARUS ultra-widefield images shows agreement with 7-fields colour fundus photography.
    Santos AR; Ghate S; Lopes M; Rocha AC; Santos T; Reste-Ferreira D; Manivannan N; Foote K; Cunha-Vaz J
    BMC Ophthalmol; 2024 Sep; 24(1):387. PubMed ID: 39227901
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy.
    Cai S; Parker F; Urias MG; Goldberg MF; Hager GD; Scott AW
    JAMA Ophthalmol; 2021 Feb; 139(2):206-213. PubMed ID: 33377944
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Ultra-widefield color fundus photography combined with high-speed ultra-widefield swept-source optical coherence tomography angiography for non-invasive detection of lesions in diabetic retinopathy.
    Li J; Wei D; Mao M; Li M; Liu S; Li F; Chen L; Liu M; Leng H; Wang Y; Ning X; Liu Y; Dong W; Zhong J
    Front Public Health; 2022; 10():1047608. PubMed ID: 36408020
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Nonmydriatic Ultra-Widefield Fundus Photography in a Hematology Clinic Shows Utility for Screening of Sickle Cell Retinopathy.
    Ahmed I; Pradeep T; Goldberg MF; Liu TYA; Aradhya A; Montana MP; Photiadis N; Williams E; Smith B; Tian J; Lanzkron SM; Scott AW
    Am J Ophthalmol; 2022 Apr; 236():241-248. PubMed ID: 34780794
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection.
    Liu L; Hong J; Wu Y; Liu S; Wang K; Li M; Zhao L; Liu Z; Li L; Cui T; Tsui CK; Xu F; Hu W; Yun D; Chen X; Shang Y; Bi S; Wei X; Lai Y; Lin D; Fu Z; Deng Y; Cai K; Xie Y; Cao Z; Wang D; Zhang X; Dongye M; Lin H; Wu X
    Br J Ophthalmol; 2024 Sep; 108(10):1423-1429. PubMed ID: 38839251
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.
    Wu T; Ju L; Fu X; Wang B; Ge Z; Liu Y
    Transl Vis Sci Technol; 2024 Feb; 13(2):1. PubMed ID: 38300623
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.
    Cen LP; Ji J; Lin JW; Ju ST; Lin HJ; Li TP; Wang Y; Yang JF; Liu YF; Tan S; Tan L; Li D; Wang Y; Zheng D; Xiong Y; Wu H; Jiang J; Wu Z; Huang D; Shi T; Chen B; Yang J; Zhang X; Luo L; Huang C; Zhang G; Huang Y; Ng TK; Chen H; Chen W; Pang CP; Zhang M
    Nat Commun; 2021 Aug; 12(1):4828. PubMed ID: 34376678
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales.
    Cui T; Lin D; Yu S; Zhao X; Lin Z; Zhao L; Xu F; Yun D; Pang J; Li R; Xie L; Zhu P; Huang Y; Huang H; Hu C; Huang W; Liang X; Lin H
    JAMA Ophthalmol; 2023 Nov; 141(11):1045-1051. PubMed ID: 37856107
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.
    Tang F; Luenam P; Ran AR; Quadeer AA; Raman R; Sen P; Khan R; Giridhar A; Haridas S; Iglicki M; Zur D; Loewenstein A; Negri HP; Szeto S; Lam BKY; Tham CC; Sivaprasad S; Mckay M; Cheung CY
    Ophthalmol Retina; 2021 Nov; 5(11):1097-1106. PubMed ID: 33540169
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Widefield OCT-Angiography and Fluorescein Angiography Assessments of Nonperfusion in Diabetic Retinopathy and Edema Treated with Anti-Vascular Endothelial Growth Factor.
    Couturier A; Rey PA; Erginay A; Lavia C; Bonnin S; Dupas B; Gaudric A; Tadayoni R
    Ophthalmology; 2019 Dec; 126(12):1685-1694. PubMed ID: 31383483
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Widefield oct-angiography-based classification of sickle cell retinopathy.
    Bistour A; Mehanna CJ; Chuttarsing B; Colantuono D; Amoroso F; Beaumont W; Matri KE; Souied EH; Miere A
    Graefes Arch Clin Exp Ophthalmol; 2023 Oct; 261(10):2805-2812. PubMed ID: 37219613
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A deep learning framework for the early detection of multi-retinal diseases.
    Ejaz S; Baig R; Ashraf Z; Alnfiai MM; Alnahari MM; Alotaibi RM
    PLoS One; 2024; 19(7):e0307317. PubMed ID: 39052616
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography.
    Oh R; Lee EK; Bae K; Park UC; Yu HG; Yoon CK
    Korean J Ophthalmol; 2023 Apr; 37(2):95-104. PubMed ID: 36758539
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.
    Sedova A; Hajdu D; Datlinger F; Steiner I; Neschi M; Aschauer J; Gerendas BS; Schmidt-Erfurth U; Pollreisz A
    Eye (Lond); 2022 Mar; 36(3):510-516. PubMed ID: 35132211
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep learning-based classification of retinal atrophy using fundus autofluorescence imaging.
    Miere A; Capuano V; Kessler A; Zambrowski O; Jung C; Colantuono D; Pallone C; Semoun O; Petit E; Souied E
    Comput Biol Med; 2021 Mar; 130():104198. PubMed ID: 33383315
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep Learning-Based Automated Detection of Retinal Breaks and Detachments on Fundus Photography.
    Christ M; Habra O; Monnin K; Vallotton K; Sznitman R; Wolf S; Zinkernagel M; Márquez Neila P
    Transl Vis Sci Technol; 2024 Apr; 13(4):1. PubMed ID: 38564203
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study.
    Lin D; Xiong J; Liu C; Zhao L; Li Z; Yu S; Wu X; Ge Z; Hu X; Wang B; Fu M; Zhao X; Wang X; Zhu Y; Chen C; Li T; Li Y; Wei W; Zhao M; Li J; Xu F; Ding L; Tan G; Xiang Y; Hu Y; Zhang P; Han Y; Li JO; Wei L; Zhu P; Liu Y; Chen W; Ting DSW; Wong TY; Chen Y; Lin H
    Lancet Digit Health; 2021 Aug; 3(8):e486-e495. PubMed ID: 34325853
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DETECTION AND LOCALIZATION OF RETINAL BREAKS IN ULTRAWIDEFIELD FUNDUS PHOTOGRAPHY USING a YOLO v3 ARCHITECTURE-BASED DEEP LEARNING MODEL.
    Oh R; Oh BL; Lee EK; Park UC; Yu HG; Yoon CK
    Retina; 2022 Oct; 42(10):1889-1896. PubMed ID: 36129265
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Assessment of diabetic retinopathy using two ultra-wide-field fundus imaging systems, the Clarus® and Optos™ systems.
    Hirano T; Imai A; Kasamatsu H; Kakihara S; Toriyama Y; Murata T
    BMC Ophthalmol; 2018 Dec; 18(1):332. PubMed ID: 30572870
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.