Enhanced Trauma Video Review With Computer Vision: Trauma Resuscitation Phase Segmentation and Procedure Detection

To develop and evaluate a computer vision model for automating the identification of trauma resuscitation phases and procedures during trauma video review (TVR).

TVR is a valuable tool for assessing trauma resuscitation quality and identifying improvement opportunities. However, its labor-intensive nature limits widespread adoption.

Ninety-five de-identified trauma resuscitation videos from a Level I trauma center were analyzed. Thirty videos (32%) were manually annotated to define 4 trauma phases-pre-arrival, paramedic handover, acute resuscitation, and pre-departure-and procedures, including X-rays, ultrasound, and intravenous access. A multi-institutional research group guided the annotation framework development. Interrater reliability was assessed using temporal intersection over union (tIoU). Model performance metrics included frame-wise accuracy, edit score, F1 scores (tIoU thresholds: 0.1, 0.25, 0.5), average precision (AP), and average recall.

The cohort included 65 (68.4%) male patients, median [interquartile range (IQR)] age 31 (23-44.5) years, with 75 (78.9%) blunt injuries and a median (IQR) injury severity score of 22 (12-29). Annotators achieved a high interrater reliability [mean (standard deviation) tIoU: 0.89 (0.19)]. The model achieved a frame-wise accuracy of 98.3%, edit score of 92.1%, and F1 scores of 94.5%, 94.1%, and 86.3% at tIoU thresholds of 0.1, 0.25, and 0.5, respectively. Procedure detection AP exceeded 66% for X-rays and central line placements.

Computer vision can effectively automate TVR, enabling accurate phase segmentation and procedure detection. This approach has the potential to streamline TVR, promote adoption, and improve trauma care quality.