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Journal Abstract Search

137 related items for PubMed ID: 36121622

  • 1. Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.
    Toda N, Hashimoto M, Iwabuchi Y, Nagasaka M, Takeshita R, Yamada M, Yamada Y, Jinzaki M.
    Jpn J Radiol; 2023 Jan; 41(1):38-44. PubMed ID: 36121622
    [Abstract] [Full Text] [Related]

  • 2. Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study.
    Ueda D, Yamamoto A, Shimazaki A, Walston SL, Matsumoto T, Izumi N, Tsukioka T, Komatsu H, Inoue H, Kabata D, Nishiyama N, Miki Y.
    BMC Cancer; 2021 Oct 18; 21(1):1120. PubMed ID: 34663260
    [Abstract] [Full Text] [Related]

  • 3. Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers.
    Szucs-Farkas Z, Patak MA, Yuksel-Hatz S, Ruder T, Vock P.
    Eur Radiol; 2010 Jun 18; 20(6):1289-96. PubMed ID: 19936752
    [Abstract] [Full Text] [Related]

  • 4. 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 18; 30(9):4943-4951. PubMed ID: 32350657
    [Abstract] [Full Text] [Related]

  • 5. Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.
    Liang CH, Liu YC, Wu MT, Garcia-Castro F, Alberich-Bayarri A, Wu FZ.
    Clin Radiol; 2020 Jan 18; 75(1):38-45. PubMed ID: 31521323
    [Abstract] [Full Text] [Related]

  • 6. 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 18; 290(1):218-228. PubMed ID: 30251934
    [Abstract] [Full Text] [Related]

  • 7. 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]

  • 8. Computer-Aided Detection of Seven Chest Pathologies on Standard Posteroanterior Chest X-Rays Compared to Radiologists Reading Dual-Energy Subtracted Radiographs.
    Fischer G, De Silvestro A, Müller M, Frauenfelder T, Martini K.
    Acad Radiol; 2022 Aug 10; 29(8):e139-e148. PubMed ID: 34706849
    [Abstract] [Full Text] [Related]

  • 9. Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.
    Sim Y, Chung MJ, Kotter E, Yune S, Kim M, Do S, Han K, Kim H, Yang S, Lee DJ, Choi BW.
    Radiology; 2020 Jan 10; 294(1):199-209. PubMed ID: 31714194
    [Abstract] [Full Text] [Related]

  • 10. Value of a Computer-aided Detection System Based on Chest Tomosynthesis Imaging for the Detection of Pulmonary Nodules.
    Yamada Y, Shiomi E, Hashimoto M, Abe T, Matsusako M, Saida Y, Ogawa K.
    Radiology; 2018 Apr 10; 287(1):333-339. PubMed ID: 29206596
    [Abstract] [Full Text] [Related]

  • 11. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance.
    de Hoop B, De Boo DW, Gietema HA, van Hoorn F, Mearadji B, Schijf L, van Ginneken B, Prokop M, Schaefer-Prokop C.
    Radiology; 2010 Nov 10; 257(2):532-40. PubMed ID: 20807851
    [Abstract] [Full Text] [Related]

  • 12. Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
    Murchison JT, Ritchie G, Senyszak D, Nijwening JH, van Veenendaal G, Wakkie J, van Beek EJR.
    PLoS One; 2022 Nov 10; 17(5):e0266799. PubMed ID: 35511758
    [Abstract] [Full Text] [Related]

  • 13. 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 10; 166():111002. PubMed ID: 37499478
    [Abstract] [Full Text] [Related]

  • 14. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.
    Hirose T, Nitta N, Shiraishi J, Nagatani Y, Takahashi M, Murata K.
    Acad Radiol; 2008 Dec 10; 15(12):1505-12. PubMed ID: 19000867
    [Abstract] [Full Text] [Related]

  • 15. Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.
    Choi SY, Park S, Kim M, Park J, Choi YR, Jin KN.
    Medicine (Baltimore); 2021 Apr 23; 100(16):e25663. PubMed ID: 33879750
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  • 19. Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.
    Schalekamp S, van Ginneken B, Koedam E, Snoeren MM, Tiehuis AM, Wittenberg R, Karssemeijer N, Schaefer-Prokop CM.
    Radiology; 2014 Jul 23; 272(1):252-61. PubMed ID: 24635675
    [Abstract] [Full Text] [Related]

  • 20. Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study.
    De Boo DW, Uffmann M, Weber M, Bipat S, Boorsma EF, Scheerder MJ, Freling NJ, Schaefer-Prokop CM.
    Acad Radiol; 2011 Dec 23; 18(12):1507-14. PubMed ID: 21963532
    [Abstract] [Full Text] [Related]

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