Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system
- 1General Surgery, Shaanxi Nuclear Industry 215 Hospital, Xianyang 712000, Shaanxi, China.
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.
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BibTeX
@article{zhang2026radiographic,
title = {{Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system}},
author = {Xiaodi Zhang},
journal = {APL bioengineering},
year = {2026},
month = nov,
doi = {10.1063/5.0351586},
url = {https://europepmc.org/article/PMC/PMC13727374},
note = {CC BY},
}
RIS
TY - JOUR
TI - Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system
AU - Xiaodi Zhang
PY - 2026
DA - 2026/11/02
JO - APL bioengineering
DO - 10.1063/5.0351586
UR - https://europepmc.org/article/PMC/PMC13727374
AB - 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.
SN - 2473-2877
ER -