{
  "abstract": "Abdominal aortic aneurysm (AAA) is a progressive dilatative disease of the abdominal aorta with high rupture-related mortality, emphasizing the need for early risk assessment. Conventional anatomical indicators, such as maximum diameter, are insufficient to characterize rupture risk, motivating the incorporation of hemodynamic parameters. Image-based CFD enables the estimation of these parameters but remains clinically limited by high computational cost and long processing times. To overcome these limitations, deep learning–based surrogate models such as MultiViewUNet have recently been proposed, though previous studies were constrained by relatively low spatial resolution and limited coverage of hemodynamic parameters. In this study, we propose MH-UNet, a multi-view, high-resolution surrogate modeling framework that extends the original MultiViewUNet to a higher input resolution and additional hemodynamic parameters. MH-UNet converts three-dimensional AAA geometries into two-dimensional images from multiple viewing angles and predicts TAWSS, OSI, RRT, and ECAP using data from 101 patients. Both single-inference and dual-inference strategies were implemented and compared. Single-inference consistently outperformed dual-inference across all parameters; the cascaded dual-inference scheme amplified errors through the nonlinear OSI and RRT formulations, resulting in negative R² values for OSI, RRT, and ECAP. Among the single-inference predictions, TAWSS was predicted most reliably (R² = 0.803), whereas OSI was the most challenging to predict. Agreement between single-inference predictions and CFD-derived results demonstrates the potential of MH-UNet as a rapid surrogate tool for estimating WSS-derived hemodynamic indices in AAA under fixed boundary conditions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Engineering, Graduate School, Pusan National University, Busan, Republic of Korea.",
    "Department of Thoracic and Cardiovascular Surgery, Pusan National University School of Medicine, Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.",
    "Department of Biomedical Engineering, School of Medicine, Pusan National University, Busan, Republic of Korea.",
    "In Silico Medicine Lab, Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "MyeungHye Suh"
    },
    {
      "affs": [
        1
      ],
      "name": "Up Huh",
      "orcid": "0000-0002-7739-3979"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Chiseung Lee",
      "orcid": "0000-0002-6840-7750"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b1k8gpbqvx7yt.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0348911",
  "fulltext_url": "https://europepmc.org/articles/PMC13716196",
  "id": "b1k8gpbqvx7yt",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0348911",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Anatomy"
    ],
    [
      "heading",
      "Aneurysms"
    ],
    [
      "heading",
      "Artificial Intelligence"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Blood"
    ],
    [
      "heading",
      "Blood Flow"
    ],
    [
      "heading",
      "Body Fluids"
    ],
    [
      "heading",
      "Classical Mechanics"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Curvature"
    ],
    [
      "heading",
      "Deep Learning"
    ],
    [
      "heading",
      "Epidemiology"
    ],
    [
      "heading",
      "Geometry"
    ],
    [
      "heading",
      "Hematology"
    ],
    [
      "heading",
      "Hemodynamics"
    ],
    [
      "heading",
      "Machine Learning"
    ],
    [
      "heading",
      "Mathematics"
    ],
    [
      "heading",
      "Mechanical Stress"
    ],
    [
      "heading",
      "Medical Risk Factors"
    ],
    [
      "heading",
      "Medicine and Health Sciences"
    ],
    [
      "heading",
      "Physical Sciences"
    ],
    [
      "heading",
      "Physics"
    ],
    [
      "heading",
      "Physiology"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Shear Stresses"
    ],
    [
      "heading",
      "Vascular Diseases"
    ],
    [
      "heading",
      "Vascular Medicine"
    ],
    [
      "openalex",
      "2740|Aortic aneurysm repair treatments"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Body fluids/Blood/Blood flow"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Body fluids/Blood/Blood flow"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Body fluids/Blood/Blood flow"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Hematology/Hemodynamics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Vascular medicine/Vascular diseases/Aneurysms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Geometry"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Geometry/Curvature"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Mechanical stress/Shear stresses"
    ]
  ],
  "openalex": "W7224092372",
  "pdf_url": "https://europepmc.org/articles/PMC13716196?pdf=render",
  "pmcid": "PMC13716196",
  "pmid": "42949920",
  "published": "2026-10-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/b1k8gpbqvx7yt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
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      "first_seen": "2026-11-01T12:23:14Z",
      "native_id": "PMC13716196",
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "heading:Biology and Life Sciences",
      "subfield": "general"
    },
    {
      "field": "mathematics",
      "origin": "plos:geometry",
      "subfield": "geometry-topology"
    },
    {
      "field": "medicine",
      "origin": "heading:Epidemiology",
      "subfield": "epidemiology-public-health"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    },
    {
      "field": "physics",
      "origin": "heading:Physics",
      "subfield": "general"
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  ],
  "title": "Artificial intelligence-based surrogate modeling of hemodynamic indicators in abdominal aortic aneurysms using computational fluid dynamics",
  "title_text": "Artificial intelligence-based surrogate modeling of hemodynamic indicators in abdominal aortic aneurysms using computational fluid dynamics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:23:14Z",
  "url": "https://research.draconitic.com/paper/b1k8gpbqvx7yt",
  "venue": "PLOS One",
  "versions": []
}