Ribtrack: Rib-Based Tracking for Multi-Planar Reconstruction: a Novel Approach to Automated Rib Multiplanar Reconstruction Using Yolov10 With Bytetrack

Rib fractures are common injuries that pose significant risks, necessitating rapid and accurate diagnosis. Traditional chest CT radiographic diagnostic techniques are highly dependent on the manual reading of images. The process is labor-intensive, highly experience-dependent, and less accurate, prompting the need for advanced methods based on artificial intelligence. To solve this problem, this study demonstrates a novel automated rib multiplanar reconstruction method that integrates object detection and tracking algorithms, specifically YOLO-X and ByteTrack. A dataset of 200 patients from Tongji Medical College, including 88 with rib fractures and 112 normal, is utilized to train and validate the algorithm model. Superior performance with high precision (0.993), recall (0.995), and mAP (0.994) can be achieved by YOLOv10-ByteTrack integration. The tracking algorithm improves the success rate by over 30 %, with a statistical error reduction of nearly 70%, greatly enhancing the detection accuracy. This study represents a significant step forward in computerized tomography reconstruction, provides a reliable solution for rib fracture diagnosis, and addresses limitations of traditional MPR, contributing to both scientific research and clinical practice.