{
  "abstract": "Stroke remains one of the leading causes of disability and mortality worldwide, where timely and accurate diagnosis is critical for guiding treatment and improving patient outcomes. However, a global shortage of trained clinicians and radiologists continues to limit rapid and reliable interpretation of neuroimaging, particularly in resource-constrained settings. Artificial intelligence (AI) has emerged as a promising solution to this challenge by enabling efficient analysis of medical images. Here we present an Integrated Stroke Diagnosis System for MRI (ISDS-MRI), a unified framework that leverages graph neural networks and sequence-specific feature modeling for comprehensive ischemic stroke analysis. This framework is designed to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI data, while accommodating incomplete combinations of MRI sequences. To ensure generalizability, we evaluate our approach across multiple publicly available MRI datasets and introduce a newly curated dataset, BGD-MRIS, comprising 532 MRI scans from three hospitals in Bangladesh. This newly curated dataset provides a multi-center MRI cohort from a resource-constrained setting, offering an additional test bed for evaluating stroke AI across heterogeneous clinical imaging protocols. Experimental results demonstrate that ISDS-MRI achieves a Dice score of 0.725 for lesion segmentation, a AUC of 0.962, and a lesion volume estimation relative error of 8.4%, outperforming comparison methods by 3.2% in Dice score and 2.6% in detection performance, while reducing volume estimation relative error by 1.9%. These results highlight the robustness and clinical potential of ISDS-MRI for scalable and comprehensive stroke diagnosis from MRI. The BGD-MRIS dataset will be publicly available at https://github.com/Zhicheng-Lu/stroke_mri.",
  "abstract_shown": true,
  "affiliations": [
    "University of Queensland"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 21,
  "authors": [
    {
      "name": "Z. Lu"
    },
    {
      "name": "S. Uddin"
    },
    {
      "name": "S. Uribe"
    },
    {
      "name": "S. White"
    },
    {
      "name": "R. T. Martins"
    },
    {
      "name": "S. Chau"
    },
    {
      "name": "A. S. M. Mosaddek"
    },
    {
      "name": "M. S. Islam"
    },
    {
      "name": "N. Nahar"
    },
    {
      "name": "A. K. M. Azad"
    },
    {
      "name": "K. M. N. Hossain"
    },
    {
      "name": "H. S. Choudhury"
    },
    {
      "name": "K. M. R. Hasan"
    },
    {
      "name": "N. Mosaddek"
    },
    {
      "name": "S. Rahman"
    },
    {
      "name": "M. M. Hossain"
    },
    {
      "name": "K. M. M. H. Sizar"
    },
    {
      "name": "C. Angione"
    },
    {
      "name": "P. Lio"
    },
    {
      "name": "M. T. Islam"
    },
    {
      "affs": [
        0
      ],
      "name": "M. A. Moni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wd2vskvvktcv.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.12.26362795",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1.full",
  "id": "6wd2vskvvktcv",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7213255129",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6wd2vskvvktcv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:33Z",
      "native_id": "10.64898/2026.09.12.26362795",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.12.26362795v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences",
  "title_text": "Ischemic Stroke Detection, Segmentation, and Volume Estimation using Multi-sequence MRI Data with Missing Sequences",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:33Z",
  "url": "https://research.draconitic.com/paper/6wd2vskvvktcv",
  "venue": "medRxiv",
  "versions": []
}