Machine learning-based tool may help plan new treatment in patients with adolescent idiopathic scoliosis

The study, published in PLOS One, proposed a machine-learning-based tool for planning anterior vertebral body tethering (AVBT), an emerging minimally invasive surgical treatment.

“The current model has the potential to serve as a valuable clinical tool, providing insight into the optimal timing of intervention and surgical planning parameters,” Sriram Balasubramanian, Drexel University, Philadelphia, PA, United States of America, and colleagues wrote.

AVBT, which was approved in the US in 2019, involves flexible cord implantation along the spine to guide spinal growth to correct deformities in adolescent patients who continue to progress despite bracing. In several studies, the tool was shown to be promising but, despite this, the predictability of the procedure remains uncertain and hinges on a complex interplay of factors that are difficult to analyze clinically.

Based on the above background, the research team developed a machine-learning-based algorithm that could potentially fill this gap.

Data from 91 patients with adolescent idiopathic scoliosis who underwent AVBT surgery at the Shriners Hospitals for Children in Philadelphia, were included. For all patients, spinal X-rays were taken at six visits, from patients’ first standing X-rays to their most recent follow-up exam.

The researchers analyzed these images, as well as surgical and demographic features associated with them to identify the 11 most predictive features of subsequent AVBT corrections and trained a gradient boosting regressor (GBR) model to predict outcomes based on these features. The dataset was randomly split into training (80%) and testing (20%) datasets.

The researchers reported the following findings:

• The AI model predicted the final Cobb angle with an average error of 6.3 ± 5.6 degrees.
• The model also provided a prediction interval, where 84% of the actual values were within the 90% prediction interval.
• The GBR model, trained on these features, predicted the final curve magnitude with a clinically acceptable margin of error.

"This is the first study to apply AI methods to longitudinal data from patients who underwent AVBT surgery," the researchers wrote. "Significantly, the model is based on a rank-ordered list of the most predictive features associated with postsurgical curve correction."

"The current model has the potential to serve as a valuable clinical tool, providing insight into the optimal timing of intervention and surgical planning parameters, which may improve surgical outcomes and facilitate informed decision-making in patient selection and timing for AVBT surgery," they concluded.

Reference:

Alfraihat, A., Samdani, A. F., & Balasubramanian, S. (2024). Predicting radiographic outcomes of vertebral body tethering in adolescent idiopathic scoliosis patients using machine learning. PLOS ONE, 19(1), e0296739. https://doi.org/10.1371/journal.pone.0296739