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Journal Abstract Search

134 related items for PubMed ID: 37750179

  • 1. External validation of deep learning-based automated detection algorithm for chest radiograph: practical issues in outpatient clinic.
    Lee DE, Chae KJ, Jin GY, Park SY, Jeong JS, Ahn SY.
    Acta Radiol; 2023 Nov; 64(11):2898-2907. PubMed ID: 37750179
    [Abstract] [Full Text] [Related]

  • 2. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs.
    Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, Park CM.
    Radiology; 2019 Jan; 290(1):218-228. PubMed ID: 30251934
    [Abstract] [Full Text] [Related]

  • 3. Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.
    Jang S, Song H, Shin YJ, Kim J, Kim J, Lee KW, Lee SS, Lee W, Lee S, Lee KH.
    Radiology; 2020 Sep; 296(3):652-661. PubMed ID: 32692300
    [No Abstract] [Full Text] [Related]

  • 4. Deep learning-based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals.
    Lee JH, Park S, Hwang EJ, Goo JM, Lee WY, Lee S, Kim H, Andrews JR, Park CM.
    Eur Radiol; 2021 Feb; 31(2):1069-1080. PubMed ID: 32857202
    [Abstract] [Full Text] [Related]

  • 5. Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs.
    Nam JG, Kim M, Park J, Hwang EJ, Lee JH, Hong JH, Goo JM, Park CM.
    Eur Respir J; 2021 May; 57(5):. PubMed ID: 33243843
    [Abstract] [Full Text] [Related]

  • 6. Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs.
    Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, Goo JM, Aum J, Yim JJ, Cohen JG, Ferretti GR, Park CM, DLAD Development and Evaluation Group.
    JAMA Netw Open; 2019 Mar 01; 2(3):e191095. PubMed ID: 30901052
    [Abstract] [Full Text] [Related]

  • 7. Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.
    Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, Goo JM, Aum J, Yim JJ, Park CM, Deep Learning-Based Automatic Detection Algorithm Development and Evaluation Group.
    Clin Infect Dis; 2019 Aug 16; 69(5):739-747. PubMed ID: 30418527
    [Abstract] [Full Text] [Related]

  • 8. Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location.
    Ueno M, Yoshida K, Takamatsu A, Kobayashi T, Aoki T, Gabata T.
    Eur J Radiol; 2023 Sep 16; 166():111002. PubMed ID: 37499478
    [Abstract] [Full Text] [Related]

  • 9. Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs.
    Huh JE, Lee JH, Hwang EJ, Park CM.
    Korean J Radiol; 2023 Feb 16; 24(2):155-165. PubMed ID: 36725356
    [Abstract] [Full Text] [Related]

  • 10. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.
    Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP.
    PLoS Med; 2018 Nov 16; 15(11):e1002686. PubMed ID: 30457988
    [Abstract] [Full Text] [Related]

  • 11. Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.
    Guo L, Zhou C, Xu J, Huang C, Yu Y, Lu G.
    J Imaging Inform Med; 2024 Jun 16; 37(3):922-934. PubMed ID: 38332402
    [Abstract] [Full Text] [Related]

  • 12. Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.
    Kim H, Park CM, Goo JM.
    Eur Radiol; 2020 Apr 16; 30(4):2346-2355. PubMed ID: 31900698
    [Abstract] [Full Text] [Related]

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  • 15. Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study.
    Sung J, Park S, Lee SM, Bae W, Park B, Jung E, Seo JB, Jung KH.
    Radiology; 2021 May 16; 299(2):450-459. PubMed ID: 33754828
    [No Abstract] [Full Text] [Related]

  • 16. Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population.
    Lee JH, Sun HY, Park S, Kim H, Hwang EJ, Goo JM, Park CM.
    Radiology; 2020 Dec 16; 297(3):687-696. PubMed ID: 32960729
    [Abstract] [Full Text] [Related]

  • 17. DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.
    Wehbe RM, Sheng J, Dutta S, Chai S, Dravid A, Barutcu S, Wu Y, Cantrell DR, Xiao N, Allen BD, MacNealy GA, Savas H, Agrawal R, Parekh N, Katsaggelos AK.
    Radiology; 2021 Apr 16; 299(1):E167-E176. PubMed ID: 33231531
    [Abstract] [Full Text] [Related]

  • 18. 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 01; 3(10):e2022779. PubMed ID: 33034642
    [Abstract] [Full Text] [Related]

  • 19. Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation.
    Majkowska A, Mittal S, Steiner DF, Reicher JJ, McKinney SM, Duggan GE, Eswaran K, Cameron Chen PH, Liu Y, Kalidindi SR, Ding A, Corrado GS, Tse D, Shetty S.
    Radiology; 2020 Feb 01; 294(2):421-431. PubMed ID: 31793848
    [Abstract] [Full Text] [Related]

  • 20. An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study.
    Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, Conjeti S, Ridder K, Sistermanns M, Wang L, Preuhs A, Ghesu F, Mansoor A, Moghbel M, Botwin A, Singh R, Cartmell S, Patti J, Huemmer C, Fieselmann A, Joerger C, Mirshahzadeh N, Muse V, Kalra M.
    JAMA Netw Open; 2021 Dec 01; 4(12):e2141096. PubMed ID: 34964851
    [Abstract] [Full Text] [Related]

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