{
  "abstract": "The current clinical gold standard for identifying vulnerable plaque is plaque Magnetic Resonance Imaging (MRI). However, owing to its limited availability, many patients do not have access. This pilot study aims to explore whether computational fluid dynamics (CFD) modelling, established using computed tomography angiography (CTA) and neurovascular ultrasound measurements (NVUS), as part of current clinical routine, can be used to triage vulnerable plaque in patients with carotid atherosclerotic stenosis. Nine patients who underwent evaluation for carotid endarterectomy between January 2021 and March 2023 were retrospectively included. Carotid geometries were segmented from CTA and reconstructed for CFD simulations using patient-specific NVUS-derived boundary conditions. Geometric and novel hemodynamic features were extracted. A logistic regression (LR) and a support vector machine (SVM) classifier with leave-one-out cross-validation and embedded feature selection were trained to identify vulnerable plaque, assessed according to the Plaque-Reporting and Data System based on multi-modal plaque MRI findings. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall AUC, confusion matrix, and other related metrics. Eighteen carotid arteries (9/18 were vulnerable) were analyzed. The classifiers achieved mean ROC-AUC of 0.77 and 0.78 for the LR and SVM, respectively. Key risk factors identified by both classifiers included mean helicity (OR: 0.5, 95%CI: 0.42, 0.58; mean feature coefficient: -0.27, 95%CI: -0.39, -0.15), kurtosis/skewness of helicity, and skewness of streamwise vorticity. Patient-level and sub-cohort analyses also support these findings. This pilot study demonstrated that combining NVUS and CTA with CFD may facilitate the identification of vulnerable plaques. Higher order statistics of intravascular hemodynamics may serve as promising surrogate features of plaque vulnerability. Author summary: This study explored a new way to identify vulnerable carotid plaque, which contributes to one in every five stroke cases. Currently, the best method for detecting these plaques is through plaque magnetic resonance imaging (MRI), but it is unavailable in many clinics. In this study, we used a combination of standard clinical imaging modalities and computer modeling methods to simulate blood flow in the arteries. Information about the blood flow was extracted from these results, and machine learning models were trained to predict which patients had vulnerable plaques. Our results indicate that our method has the potential to identify high-risk cases for patients without access to MRI and might help reduce the risk of stroke. This approach could eventually offer a more accessible alternative to MRI for identifying dangerous carotid plaques.",
  "abstract_shown": true,
  "affiliations": [
    "ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland",
    "Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland",
    "Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland",
    "Department of Neurosurgery, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland",
    "Department of Neurology and Neurophysiology, Faculty of Medicine, Medical Center-University of Freiburg, Freiburg, Germany"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shaokai Zheng"
    },
    {
      "affs": [
        1
      ],
      "name": "David Bervini"
    },
    {
      "affs": [
        2
      ],
      "name": "Hugo Besson"
    },
    {
      "affs": [
        3
      ],
      "name": "Sofía González Martínez"
    },
    {
      "affs": [
        4
      ],
      "name": "Mirjam R. Heldner"
    },
    {
      "name": "Mandy Müller"
    },
    {
      "name": "Thomas Meinel"
    },
    {
      "name": "Johannes Kaesmacher"
    },
    {
      "name": "Andreas Harloff"
    },
    {
      "name": "Dominik Obrist"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ae4vs4tedef6.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001762",
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  "id": "1ae4vs4tedef6",
  "issns": [
    "2767-3170"
  ],
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
    [
      "openalex",
      "2740|Cerebrovascular and Carotid Artery Diseases"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Cardiovascular anatomy/Blood vessels/Arteries/Carotid arteries"
    ],
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      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Support vector machines"
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      "/Medicine and health sciences/Anatomy/Cardiovascular anatomy/Blood vessels/Arteries/Carotid arteries"
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      "/Medicine and health sciences/Cardiology/Systole"
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      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Stenosis"
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      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Magnetic resonance imaging"
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      "/Medicine and health sciences/Hematology/Hemodynamics"
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      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Magnetic resonance imaging"
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  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001762&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1ae4vs4tedef6.ris",
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    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pdig.0001762",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cardiology",
      "subfield": "cardiovascular"
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  "title": "Data driven approach to identify vulnerable plaques in carotid atherosclerosis using computed tomography imaging and computational fluid dynamics: A pilot study",
  "title_text": "Data driven approach to identify vulnerable plaques in carotid atherosclerosis using computed tomography imaging and computational fluid dynamics: A pilot study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/1ae4vs4tedef6",
  "venue": "PLOS Digital Health",
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