Multi-view real-time detection of patient arrival in trauma resuscitation using computer vision

Every minute delay in life-saving intervention increases mortality risk in injured patients. Given this relationship, quality measurement of trauma resuscitation includes the timing of provider decisions and interventions. The current approach of manual recording of patient arrival and other timestamps may be inaccurate due to providers underestimating elapsed time during resuscitation. We introduce a frame-based computer vision system to automatically detect and classify two specific phases of patient arrival: (1) when the patient enters the trauma resuscitation room, and (2) when the patient is moved onto the bed. The proposed system consists of two stages. The first stage uses a pattern-based method to detect when a patient enters the room. The second stage uses both side-view and top-view video feeds to determine when the patient has been moved to the bed, addressing occlusion issues and enhancing robustness and precision. To minimize labeling effort and accelerate detection, we introduce PA-YOLO, a frame-based classification model, in both stages of the system. We evaluated our system using 5-fold cross-validation on 60 trauma resuscitation cases. Our results show that this approach achieves an accuracy of 0.92 ± 0.01 for patient-at-door detection and 0.93 ± 0.03 for patient-on-bed detection, with an average detection delay of 2.15 ± 1.24 s. The proposed system outperforms SlowFast by up to 5 percentage points and our previous I3D-based system by up to 7 percentage points in detection accuracy, while reducing detection delay from over 5 s to about 2 s. Compared to baseline model YOLO11, our PA-YOLO improves detection performance by 2.0% while reducing floating point operations per second (FLOPS) by 51.5%.