Intestinal ultrasound (IUS) is increasingly used for real-time assessment of inflammatory bowel disease (IBD) but quantitative analysis with artificial intelligence (AI) remains limited to bowel wall thickness (BWT) and single-frame segmentation, failing to exploit the temporal information in cineloops (CLs). The aim here was to evaluate the accuracy of an AI model compared to expert central readers using IUS CLs.

We developed a novel model using a semi-supervised algorithm combined with a temporal module, trained on 680,000 IUS images from 1572 CLs (327 patients from multiple centres). 1205 frames from 403 CLs on 94 patients were labelled by 8 domain experts (203 validation, 1002 training images). The CLs had various segments per patient with active IBD, and included axial, longitudinal and oblique sections. An external test set (27 patients, 58 CLs) of active Crohn’s patients (ascending, transverse, descending colon), centrally read by 3 expert readers, was used for initial evaluation. The model was optimized to segment 9 categories including bowel wall (BW), psoas, iliac vessels (IV), and inflamed mesentery (IM). Each output was based on a current frame and context representations from previous frames. The model continuously segments and tracks anatomy, generating temporally coherent delineations and data that feed intra- and inter-frame measurement algorithms. On the validation set, we calculated the Dice similarity coefficient (DSC), an overlap-based metric, from the expert annotations. Intraclass correlation coefficient (ICC) scores from ground truth measurements were made for every labelled image.

Qualitative evaluation by domain experts confirmed consistency and anatomical precision of the outputs across a range of bowel regions and disease activity. Quantitatively, on the validation set our model achieved DSC of 0.73 [0.70 - 0.75], 0.26 [0.23 - 0.28], 0.62 [0.55 - 0.68], 0.63 [0.53 - 0.72], 0.72 [0.64 - 0.79] and 0.41 [0.32 - 0.49] for BW, peritoneal lining, rectus, psoas, IV and IM. Based on BW segmentations, the measurement algorithm achieved an ICC of 0.83 [0.77 - 0.87] between predictions and per frame expert measurements. On the external test dataset, the ICC between an aggregated AI-driven measurement per CL and the mean of all reader measurements was 0.83 [0.73 - 0.9].

Unlike prior approaches focused on static frames or derived BWT estimation, this method preserves frame-to-frame continuity, enabling reproducible analysis of CLs with no manual pre-selection of measurement areas. Our model provides identification of many anatomical structures in addition to BWT, essential to moving AI beyond this measure and towards fully automated disease activity monitoring.