Machine learning and lumbar spondylolisthesis

Abstract

While lumbar spondylolisthesis is one of the most common conditions cared for by spine surgeons, there remains limited evidence guiding its diagnosis, classification, and management. The diversity in its underlying causes, clinical manifestations, and spinal anatomical variations poses a particular challenge in making informed clinical decisions. Machine learning (ML) methods offer novel opportunities to address these challenges by leveraging data-driven approaches. This chapter provides a comprehensive overview of the potential applications of ML in the field of lumbar spondylolisthesis. ML is a branch of artificial intelligence that employs statistical algorithms to mimic human learning behavior. In the domain of diagnosis, ML methods have been applied to detect spondylolisthesis using medical imaging. In particular, deep learning models have shown high accuracy in detecting spondylolisthesis from X-rays and MRIs, suggesting ML's potential as a diagnostic tool. Additionally, ML can aid in distinguishing spondylolisthesis grades and subtypes. Although automatic grading remains challenging, recent advances suggest that emerging ML techniques may be effective in classifying spondylolisthesis subtypes and guiding subsequent decision-making. Already, ML has been used to predict spondylolisthesis treatment outcomes, such as functional recovery and hospital length of stay. While promising, most of these prediction studies used "shallow" ML techniques, suggesting that further gains may be realized by applying deep learning methods to larger, complex datasets. In conclusion, ML advances hold promise in spondylolisthesis diagnosis, classification, and outcome prediction. In the future, these methods may help support more personalized and effective management of lumbar spondylolisthesis.