Accurate assessment of gastric content from ultrasound is critical for stratifying aspiration risk at induction of general anesthesia. However, traditional methods rely on manual tracing of gastric antra and empirical formulas, which face significant limitations in both efficiency and accuracy. To address these challenges, a novel two-stage probability map-guided dual-branch fusion framework (REASON) for gastric content assessment is proposed. In stage 1, a segmentation model generates probability maps that suppress artifacts and highlight gastric anatomy. In stage 2, a dual-branch classifier fuses information from two standard views, right lateral decubitus (RLD) and supine (SUP), to improve the discrimination of learned features. Experimental results on a self-collected dataset demonstrate that the proposed framework outperforms current state-of-the-art approaches by a significant margin. This framework shows great promise for automated preoperative aspiration risk assessment, offering a more robust, efficient, and accurate solution for clinical practice.
@article{arxiv.2511.01302,
title = {REASON: Probability map-guided dual-branch fusion framework for gastric content assessment},
author = {Nu-Fnag Xiao and De-Xing Huang and Le-Tian Wang and Mei-Jiang Gui and Qi Fu and Xiao-Liang Xie and Shi-Qi Liu and Shuangyi Wang and Zeng-Guang Hou and Ying-Wei Wang and Xiao-Hu Zhou},
journal= {arXiv preprint arXiv:2511.01302},
year = {2025}
}