Assessing the Utility and Challenges of Machine Learning in Spinal Deformity Management

Study design. Systematic review Objective. To review the application of machine learning (ML) in scoliosis management, while addressing the limitations and challenges in implementing ML in scoliosis practice. Summary of Background Data. ML has revolutionized medical research, particularly in scoliosis management, enabling automated assessment of diagnosis, disease progression, and treatment response. Methods. A systematic review of the literature was conducted following the PRISMA guidelines and was registered in the PROSPERO database. The literature search included the PubMed, Embase, and Google Scholar databases. Studies since 2019 were included if they used ML models in scoliosis management. Studies that did not meet the inclusion criteria were excluded. Data extraction focused on the scoliosis type, sample size, model utility, ML model architecture, model development, training data, cross-validation, single- or multi-center study design, and study/model limitations. Results. The study included 63 articles that underwent full-text review. The most commonly studied disorders were adolescent idiopathic scoliosis (n=48) and adult spinal deformity (n=15), with five studies including other disorders and one not specifying. The uses of ML were diagnosis/classification (n=38), operative outcomes (n=11), prognosis (n=7), and risk assessment (n=7). The most used models included convolutional neural networks (n=38), random forest (n=11), and support vector machines (n=9). Only 16 studies reported external validation, while none implemented their model in practice. Common limitations reported were small sample size, artifacts obscuring images, potential selection bias, limited variety in disease severity, exclusion of parameters, lack of patient diversity, varying accuracy with curve site and severity, retrospective study, and lack of external validation. Conclusion. The current state of machine learning in scoliosis management is promising, showing potential for improving diagnosis and optimizing treatments. However, significant challenges remain. To fully understand the benefits of ML, larger, multi-center studies with more validation and effective implementation strategies are needed.