Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and motility rhythm via bedside ultrasound: a pilot study.

The cross-sectional area (CSA) of the gastric antrum and its motility rhythm reflects the gastrointestinal function of critically ill patients. Monitoring the CSA and motility rhythm is crucial but remains time-consuming and operator dependent. This study aimed to develop an artificial intelligence (AI) system for real-time automated recognition of the gastric antrum CSA and motility rhythm using bedside ultrasound. Gastric antrum ultrasound videos were prospectively collected from West China Hospital to establish training and validation datasets. The AI system's predictions were validated against senior clinicians' annotations to assess accuracy. Additionally, videos were collected to evaluate the performance of the AI system. The antrum motility rhythms of patients and volunteers were preliminarily classified to lay the foundation for the subsequent establishment of gastrointestinal motility rhythm phenotypes in critically ill patients. A total of 907 videos (620 patients and 287 volunteers) were included to develop and validate the AI system from January 2022 to November 2023. 49,240 images were used as training datasets to train the model's ability to locate and segment gastric antrum ultrasound images. The remaining 12,309 images were used as the internal validation dataset, achieving a mean dice coefficient (mDice) of 87.36% and an mean intersection over union (mIOU) of 77.56%. For the external validation dataset, 2334 images were used, resulting in mDice and mIOU values of 86.82% and 76.26%, respectively. Moreover, the AI system demonstrated robust performance in video cut frame analysis, achieving a mDice of 90.23% and a mIOU of 85.16% across 105 videos. The intraclass correlation coefficient (ICC) between human operators and the AI model was good (ICC (2, K): 0.813, 95% CI 0.728-0.871). In terms of antrum motility rhythm phenotypes, we identified several distinct patterns, such as regular movement, minimal movement, and irregular movement, reflecting different statuses, such as fasting, postmeal, postexercise, and postduty. We developed an AI system that is comparable to experienced clinicians in identifying the gastric antrum and measuring its CSA. Furthermore, the system can generate a curve representing the rhythm of antrum movement, reflecting the varying statuses of patients and volunteers. This system may optimize enteral nutrition (EN) protocols by reducing clinicians' workload and minimizing operator dependence.