Purpose: This preliminary in-vitro study was designed to evaluate the risk factors of compression injury from use of a circular stapler for end-to-end anastomosis. Methods: Transparent collagen plates were prepared in dry and wet conditions. Physical properties of collagen plates and porcine colon tissue were examined using a rheometer. Adjustable and fixed-type circular staplers were applied on the collagen plates and the gap distance and compressive pressure were measured during anvil approximation. Tissue injury was evaluated using a compression injury scale. Compression properties were accessed to optimal or overcompression based on gap distance. Results: Unacceptable injuries were rarely observed on the dry collagens, regardless of compression device. In the adjustable compression, the compressibility ratio was similar between dry and wet collagen. Overcompression and unacceptable injury increased on the wet collagens. In the fixed compression, the compressibility ratio increased significantly and unacceptable injuries were observed in more than 50% of wet collagens. Peak pressure was significantly higher in the fixed-compression types than those of adjustable type. On bivariate correlation analysis, fixed-compression type and wet collagens were respectively associated with overcompression. On multivariate analysis, edematous collagen condition was the most important risk factor and proximal anvil side, fixed compression type, and overcompression were also independent risk factors for unacceptable compression injury. Conclusion: In the edematous tissue condition, unintentional overcompression could be increased and result in tissue injury on the compression line of the circular stapler.

Medication administration via an intravenous (IV) catheter is a widely used medical procedure; however, incidents related to IV administration have consistently been reported to regulatory agencies. To improve patient safety during these incidents, it is essential to enhance the monitoring of IV administration. This study proposes an artificial intelligence-based technique for real-time infusion rate (IR) monitoring that automates several processes: the initial setup for image monitoring, the monitoring of variations in in-bag liquid volume and infusion pump settings, the determination of the relevance between infusion status and in-bag liquid residue, and the alarm processes for early detection of IV administration-related emergencies, using deep learning models and mathematical estimations. The experimental results demonstrate that the average error rate for estimating in-bag liquid volume is less than 5.00%, the average mismatch between the bag-extracted IR and the pump-extracted IR is under 3.00%, the average error rate for the “time-to-bag-empty” alarm is below 6.00%, and the error rate for the “low in-bag liquid volume” alarm is under 10.00%. The accuracy of detecting abnormal IR settings of the infusion pump was 100%. Based on these results, we conclude that the proposed artificial intelligence-based smart IR status monitoring technique shows promise as a prototype for autonomous IV administration monitoring with minimal human intervention, serving as a foundational step toward clinically deployable solutions in future healthcare settings.