Spondylolisthesis Identification in Lumbar Spine Using Centroid Distance Error

Spondylolisthesis is a spinal disorder characterized by the anterior displacement of one vertebra over the one beneath it, predominantly affecting the lumbar region. Accurate and timely diagnosis is crucial for effective treatment and patient care. This study proposes an automated method for identifying spondylolisthesis by employing a deep learning approach using SSD for lumbar vertebrae detection. After detecting the vertebrae, the centroids of each vertebra are calculated from their respective bounding boxes, and a linear model is fitted through these centroids to assess vertebral alignment. The centroid distance error, defined as the deviation of each centroid from the fitted line, is utilized to evaluate misalignment indicative of spondylolisthesis. The proposed method was evaluated on a dataset of lumbar spine images, in diagnosing spondylolisthesis based on centroid distance errors. This automated framework demonstrates significant advantages over traditional diagnostic methods, including increased efficiency, consistency, and scalability. The findings highlight the potential of integrating advanced image processing techniques and machine learning models into clinical practice, paving the way for improved diagnostic accuracy and patient outcomes in spinal disorders. Future work will focus on expanding the dataset and exploring the integration of clinical parameters to enhance the model's predictive capabilities.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup>