1. Articles mentioning both Aarhus University and National University of Ireland, Galway

    1-3 of 3
    1. Optical coherence tomography in coronary atherosclerosis assessment and intervention

      Optical coherence tomography in coronary atherosclerosis assessment and intervention
      Since optical coherence tomography (OCT) was first performed in humans two decades ago, this imaging modality has been widely adopted in research on coronary atherosclerosis and adopted clinically for the optimization of percutaneous coronary intervention. In the past 10 years, substantial advances have been made in the understanding of in vivo vascular biology using OCT. Identification by OCT of culprit plaque pathology could potentially lead to a major shift in ...
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    2. Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging

      Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging
      Background: Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels. Methods: This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention ...
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    3. Automatic Characterisation of Human Atherosclerotic Plaque Composition from Intravascular Optical Coherence Tomography Using Artificial Intelligence

      Automatic Characterisation of Human Atherosclerotic Plaque Composition from Intravascular Optical Coherence Tomography Using Artificial Intelligence
      Background Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in-vivo, but visual assessment is time-consuming and subjective. Aims This study aims to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI). Methods IVOCT pullbacks from 5 international centres were analysed in a corelab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed ...
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    4. 1-3 of 3
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  2. Organizations in the News

    1. (2 articles) Cardiovascular Research Foundation
    2. (2 articles) Harbin Medical University
    3. (2 articles) Wakayama Medical University
    4. (1 articles) Tohoku University
    5. (1 articles) University Hospital Zurich
    6. (1 articles) Harvard University
    7. (1 articles) University of Melbourne
    8. (1 articles) University of Ioannina
    9. (1 articles) University of Athens
    10. (1 articles) University of Ulsan
    11. (1 articles) Kyung Hee University
    12. (1 articles) Korea Advanced Institute of Science and Technology