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Journal Abstract Search

133 related items for PubMed ID: 31900698

  • 1. 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; 30(4):2346-2355. PubMed ID: 31900698
    [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 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; 166():111002. PubMed ID: 37499478
    [Abstract] [Full Text] [Related]

  • 5. Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
    Kim YG, Lee SM, Lee KH, Jang R, Seo JB, Kim N.
    Eur Radiol; 2020 Sep; 30(9):4943-4951. PubMed ID: 32350657
    [Abstract] [Full Text] [Related]

  • 6. 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; 24(2):155-165. PubMed ID: 36725356
    [Abstract] [Full Text] [Related]

  • 7. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.
    Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK.
    JAMA Netw Open; 2020 Sep 01; 3(9):e2017135. PubMed ID: 32970157
    [Abstract] [Full Text] [Related]

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  • 9. 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 01; 31(2):1069-1080. PubMed ID: 32857202
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  • 11. 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 01; 64(11):2898-2907. PubMed ID: 37750179
    [Abstract] [Full Text] [Related]

  • 12. 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]

  • 13. Short-term Reproducibility of Pulmonary Nodule and Mass Detection in Chest Radiographs: Comparison among Radiologists and Four Different Computer-Aided Detections with Convolutional Neural Net.
    Kim YG, Cho Y, Wu CJ, Park S, Jung KH, Seo JB, Lee HJ, Hwang HJ, Lee SM, Kim N.
    Sci Rep; 2019 Dec 10; 9(1):18738. PubMed ID: 31822774
    [Abstract] [Full Text] [Related]

  • 14. 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]

  • 15. Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.
    Teng PH, Liang CH, Lin Y, Alberich-Bayarri A, González RL, Li PW, Weng YH, Chen YT, Lin CH, Chou KJ, Chen YS, Wu FZ.
    Medicine (Baltimore); 2021 Jun 11; 100(23):e26270. PubMed ID: 34115023
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  • 17. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
    Jacobs C, van Rikxoort EM, Scholten ET, de Jong PA, Prokop M, Schaefer-Prokop C, van Ginneken B.
    Invest Radiol; 2015 Mar 11; 50(3):168-73. PubMed ID: 25478740
    [Abstract] [Full Text] [Related]

  • 18. 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]

  • 19. Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer.
    Takamatsu A, Ueno M, Yoshida K, Kobayashi T, Kobayashi S, Gabata T.
    Jpn J Radiol; 2024 Mar 01; 42(3):291-299. PubMed ID: 38032419
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