{
  "abstract": "Pneumatosis intestinalis (PI) has a wide range of etiologies, and the imaging differentiation between benign and ischemic PI is difficult, leading to a high risk of missed diagnosis. This study systematically summarizes the CT (Computed Tomography) imaging manifestations of PI and constructs an AI (Artificial Intelligence)-assisted diagnostic model that integrates multidimensional features. This study retrospectively collected CT and x-ray data from 612 patients with PI (398 benign and 214 ischemic cases) from three tertiary hospitals, and included an external validation set of 106 cases. Imaging analysis established a five-level PI-CT severity scoring system based on gas morphology, distribution, portal venous gas (PVG), intestinal wall enhancement, mesenteric edema, and ascites. The AI system adopted a two-level architecture: The first level used EfficientNet-B3 for rapid screening on x-ray films; the second level used 3D nnU-Net (No-new-Net) to segment intestinal wall gas, and then fused the data through a three-branch network (gas morphology, accompanying signs, and clinical metadata) via cross-attention to output the probability of ischemic PI. In the internal test set, the model achieved an AUC (Area Under the Curve) of 0.947 for ischemic PI (sensitivity 91.3%, specificity 93.6%), significantly outperforming senior radiologists (AUC = 0.876); the AUC on the external validation set was 0.931. SHapley Additive exPlanations analysis showed that PVG and blood lactate were the most important predictors. This two-tiered AI system can significantly improve the early identification of the causes of critical PI, providing a reliable method for intelligent diagnosis of emergency imaging.",
  "abstract_shown": true,
  "affiliations": [
    "General Surgery, Shaanxi Nuclear Industry 215 Hospital, Xianyang 712000, Shaanxi, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaodi Zhang",
      "orcid": "0000-0003-3587-5336"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/01z4zy4jpsm0e.bib",
  "cited_by": 0,
  "doi": "10.1063/5.0351586",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13727374/?tool=EBI",
  "id": "01z4zy4jpsm0e",
  "issns": [
    "2473-2877"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13727374",
  "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",
      "Articles"
    ],
    [
      "openalex",
      "2746|Abdominal vascular conditions and treatments"
    ]
  ],
  "openalex": "W7225108743",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13727374/pdf/?tool=EBI",
  "pmcid": "PMC13727374",
  "pmid": "42974629",
  "published": "2026-11-02",
  "publisher": "American Institute of Physics",
  "ris": "https://research.draconitic.com/paper/01z4zy4jpsm0e.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:51Z",
      "native_id": "PMC:PMC13727374",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13727374"
    },
    {
      "first_seen": "2026-11-04T15:20:47Z",
      "native_id": "MED:42974629",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42974629"
    },
    {
      "first_seen": "2026-11-05T12:23:28Z",
      "native_id": "PMC13727374",
      "source": "pmc",
      "url": "https://doi.org/10.1063/5.0351586"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2746|Abdominal vascular conditions and treatments",
      "subfield": "surgery"
    }
  ],
  "title": "Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system",
  "title_text": "Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:23:04Z",
  "url": "https://research.draconitic.com/paper/01z4zy4jpsm0e",
  "venue": "APL bioengineering",
  "versions": []
}