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AI-Powered X-Ray Tool Revolutionizes Scoliosis Surgery Planning

A study published in PLOS One on January 14 reveals that an AI model for X-ray imaging may assist in planning non-fusion treatments for adolescent idiopathic scoliosis patients.

Researchers from Drexel University, led by PhD candidate Ausilah Alfraihat, developed a machine learning tool for anterior vertebral body tethering (AVBT), a minimally invasive surgical treatment approved in the USA in 2019. This AI tool could help clinicians determine the best timing and parameters for intervention.

The study analyzed data from 91 patients who underwent AVBT at Shriners Children’s Hospital in Philadelphia. Using X-rays and associated patient data, the AI predicted the final spinal curvature with a mean error of 6.3 ± 5.6 degrees, providing a clinically acceptable margin of error.

This is the first study to apply AI to longitudinal data from AVBT patients, and the model shows promise as a clinical tool for improving surgical outcomes.

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