Connection

U. Schoepf to Artificial Intelligence

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This is a "connection" page, showing publications U. Schoepf has written about Artificial Intelligence.
U. Schoepf
Connection Strength
4.390
Artificial Intelligence
  1. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography. J Thorac Imaging. 2020 May; 35 Suppl 1:S35-S39.
    View in: PubMed
    Score: 0.597
  2. Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers. J Thorac Imaging. 2020 May; 35 Suppl 1:S28-S34.
    View in: PubMed
    Score: 0.597
  3. Radiologists: Protagonists of the Health Care Artificial Intelligence Revolution. J Thorac Imaging. 2020 05; 35 Suppl 1:S1-S2.
    View in: PubMed
    Score: 0.597
  4. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing. AJR Am J Roentgenol. 2020 05; 214(5):1065-1071.
    View in: PubMed
    Score: 0.590
  5. Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study. AJR Am J Roentgenol. 2022 Nov; 219(5):743-751.
    View in: PubMed
    Score: 0.173
  6. Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence. Open Heart. 2021 11; 8(2).
    View in: PubMed
    Score: 0.166
  7. Emerging methods for the characterization of ischemic heart disease: ultrafast Doppler angiography, micro-CT, photon-counting CT, novel MRI and PET techniques, and artificial intelligence. Eur Radiol Exp. 2021 03 25; 5(1):12.
    View in: PubMed
    Score: 0.159
  8. Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value. BMC Med. 2021 03 04; 19(1):55.
    View in: PubMed
    Score: 0.158
  9. Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: A validation study. Eur J Radiol. 2021 Jan; 134:109428.
    View in: PubMed
    Score: 0.155
  10. Machine Learning and Coronary Artery Calcium Scoring. Curr Cardiol Rep. 2020 07 09; 22(9):90.
    View in: PubMed
    Score: 0.151
  11. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography. J Thorac Imaging. 2020 May; 35 Suppl 1:S49-S57.
    View in: PubMed
    Score: 0.149
  12. Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives. AJNR Am J Neuroradiol. 2020 03; 41(3):373-379.
    View in: PubMed
    Score: 0.148
  13. Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease. J Cardiovasc Comput Tomogr. 2019 May - Jun; 13(3):26-33.
    View in: PubMed
    Score: 0.137
  14. The power and limitations of machine learning and artificial intelligence in cardiac CT. J Cardiovasc Comput Tomogr. 2018 May - Jun; 12(3):202-203.
    View in: PubMed
    Score: 0.130
  15. Coronary CT angiography: automatic cardiac-phase selection for image reconstruction. Eur Radiol. 2009 Aug; 19(8):1906-13.
    View in: PubMed
    Score: 0.069
  16. CNN-based evaluation of bone density improves diagnostic performance to detect osteopenia and osteoporosis in patients with non-contrast chest CT examinations. Eur J Radiol. 2023 Apr; 161:110728.
    View in: PubMed
    Score: 0.045
  17. Diabetes, Atherosclerosis, and Stenosis by AI. Diabetes Care. 2023 02 01; 46(2):416-424.
    View in: PubMed
    Score: 0.045
  18. Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning. BMC Infect Dis. 2022 Jul 21; 22(1):637.
    View in: PubMed
    Score: 0.044
  19. Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard. AJR Am J Roentgenol. 2022 09; 219(3):407-419.
    View in: PubMed
    Score: 0.043
  20. AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy. JACC Cardiovasc Imaging. 2023 02; 16(2):193-205.
    View in: PubMed
    Score: 0.042
  21. The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography. Clin Imaging. 2022 Apr; 84:149-158.
    View in: PubMed
    Score: 0.042
  22. Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study. J Thorac Imaging. 2022 May 01; 37(3):154-161.
    View in: PubMed
    Score: 0.041
  23. Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT. Acad Radiol. 2022 02; 29 Suppl 2:S108-S117.
    View in: PubMed
    Score: 0.040
  24. Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. 2020 Nov; 30(11):5841-5851.
    View in: PubMed
    Score: 0.037
  25. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019 06 21; 40(24):1975-1986.
    View in: PubMed
    Score: 0.035
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.