REASON: Probability map-guided dual-branch fusion framework for gastric content assessment
This work addresses the need for automated preoperative aspiration risk assessment in clinical practice, offering a more robust and efficient solution, though it appears incremental as it builds on existing segmentation and fusion techniques.
The paper tackled the problem of accurately assessing gastric content from ultrasound to stratify aspiration risk during anesthesia induction, proposing the REASON framework which outperformed state-of-the-art methods by a significant margin on a self-collected dataset.
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.