Radiographic manifestations of pneumatosis intestinalis and construction of an AI-assisted diagnostic system.

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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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