Current models to understand the onset and progression of scoliotic deformities in adolescent idiopathic scoliosis: a systematic review

Purpose To create an updated and comprehensive overview of the modeling studies that have been done to understand the mechanics underlying deformities of adolescent idiopathic scoliosis (AIS), to predict the risk of curve progression and thereby substantiate etiopathogenetic theories. Methods In this systematic review, an online search in Scopus and PubMed together with an analysis in secondary references was done, which yielded 86 studies. The modeling types were extracted and the studies were categorized accordingly. Results Animal modeling, together with machine learning modeling, forms the category of black box models. This category is perceived as the most clinically relevant. While animal models provide a tangible idea of the biomechanical effects in scoliotic deformities, machine learning modeling was found to be the best curve-progression predictor. The second category, that of artificial models, has, just as animal modeling, a tangible model as a result, but focusses more on the biomechanical process of the scoliotic deformity. The third category is formed by computational models, which are very popular in etiopathogenetic parameter-based studies. They are also the best in calculating stresses and strains on vertebrae, intervertebral discs, and other surrounding tissues. Conclusion This study presents a comprehensive overview of the current modeling techniques to understand the mechanics of the scoliotic deformities, predict the risk of curve progression in AIS and thereby substantiate etiopathogenetic theories. Although AIS remains to be seen as a complex and multifactorial problem, the progression of its deformity can be predicted with good accuracy. Modeling of AIS develops rapidly and may lead to the identification of risk factors and mitigation strategies in the near future. The overview presented provides a basis to follow this development.

Automated summary

This study aims to provide an updated and comprehensive overview of modeling studies on adolescent idiopathic scoliosis (AIS), focusing on understanding the mechanics of deformities, predicting curve progression, and substantiating etiopathogenetic theories. The review categorized studies into animal, machine learning, artificial, and computational models, with machine learning modeling identified as the most effective predictor of curve progression. The study concludes that although AIS is complex and multifactorial, its deformity progression can be accurately predicted and modeling techniques offer potential for risk factor identification and mitigation strategies.