1. Articles from Vladislav N. Zimin

    1-3 of 3
    1. A feasibility study of the DyeVert™ plus contrast reduction system to reduce contrast media volumes in percutaneous coronary procedures using optical coherence tomography

      A feasibility study of the DyeVert™ plus contrast reduction system to reduce contrast media volumes in percutaneous coronary procedures using optical coherence tomography

      bjective To evaluate the feasibility of using the DyeVert™ Plus EZ Contrast Reduction System in optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) procedures and to assess OCT image quality. Background OCT is employed as a powerful intravascular imaging modality; however, it requires blood displacement via contrast injection during image acquisition, thereby posing risk of nephrotoxicity. The DyeVert System is designed to reduce and facilitate monitoring of contrast media volume (CMV) delivered, without diminishing image quality. Methods We conducted a prospective clinical feasibility study to determine whether the DyeVert System is non-inferior to manual contrast injection in reducing CMV ...

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      Mentions: Hiram G. Bezerra
    2. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. Processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. Conditional random field was an important post-processing step to reduce classification errors. Sensitivities/specificities were 84.8%/97.8% and 91.4%/95.7 ...

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    3. Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than ...

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    1-3 of 3
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    1. (3 articles) Hiram G. Bezerra
    2. (2 articles) Case Western Reserve University
    3. (2 articles) David L. Wilson
    4. (1 articles) Cleveland Clinic
    5. (1 articles) Tufts University
    6. (1 articles) UCLA
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    8. (1 articles) Capital Medical University
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