{
  "abstract": "Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System (IISDS), an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [≥] 0.011 Dice score, reducing lesion volume estimation error by [≥] 0.3 average symmetric surface distance (ASSD), and increasing classification performance by [≥] 0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation. The BGD-ISD dataset will be made openly available to the research community upon publication at: https://github.com/Zhicheng-Lu/stroke_ct.",
  "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/5n1q695hat19x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.26.26360026",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26360026v1.full",
  "id": "5n1q695hat19x",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26360026v1",
  "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": "W7204812996",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26360026v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5n1q695hat19x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:25Z",
      "native_id": "10.64898/2026.08.26.26360026",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.26.26360026v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Clinically Generalisable End-to-End Graph Learning for CT Image-Based Multitask Stroke Diagnosis",
  "title_text": "Clinically Generalisable End-to-End Graph Learning for CT Image-Based Multitask Stroke Diagnosis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:25Z",
  "url": "https://research.draconitic.com/paper/5n1q695hat19x",
  "venue": "medRxiv",
  "versions": []
}