Efficient computer vision pipeline for automated anesthetic injection documentation

This study introduces a novel computer vision approach to automate documentation of anesthetic injection events in the operating room. The objective is to enhance documentation accuracy and reliability by providing precise identification of injection events and anesthetic amounts administered, while addressing stopcock placement variability. We developed a computer vision pipeline tailored for automated anesthetic injection documentation in surgical environments. The pipeline leverages the Segment Anything Model (SAM) for robust syringe segmentation, combined with vector similarity matching for generalization across different syringe sizes and occlusions. This few-shot segmentation strategy ensures generalization while minimizing annotation effort. The pipeline also integrates lightweight methods for motion detection, syringe classification, and volume estimation to ensure quasi-real-time performance. The system was tested on 304 injection events performed by 19 anesthesiologists using syringes of four sizes (3, 5, 10 and 20 ml). The pipeline achieved 100% injection-event detection sensitivity and an overall 86.3% documentation success rate. Volume estimation accuracy varied across syringe sizes, with mean absolute error (MAE) values of 0.10, 0.22, 0.37, and 0.61 ml for 3, 5, 10, and 20 ml syringes, respectively. Results compare favorably to manual measurements, which can have mean percentage errors of 1.4%-18.6%. Runtime optimization ensured quasi-real-time operation, processing each event within 10-12 s, supporting clinical workflow integration. This work presents a solution to significantly improve anesthetic injection documentation while enhancing patient safety, standardizing procedures, and reducing anesthesiologists' workload, representing a fully automated, camera-only pipeline validated on clinicians in quasi-real-time.