This study will evaluate whether a machine learning-based decision support model, called the Drum Tower Rule, can help surgeons select the lowest instrumented vertebra during corrective surgery for adolescent idiopathic scoliosis.
Patients with Lenke type 1 or Lenke type 5 adolescent idiopathic scoliosis who are scheduled for posterior spinal fusion will be randomly assigned to one of two groups. In the model-guided group, surgeons will receive the model-predicted risk of postoperative distal adding-on and a recommendation for lowest instrumented vertebra selection. In the conventional-experience group, surgeons will select the lowest instrumented vertebra according to routine clinical experience and existing surgical principles, without access to the model output.
All patients will receive standard posterior spinal fusion. The main outcome is the incidence of distal adding-on at 24 months after surgery, assessed by blinded radiographic reviewers.