Machine Learning-Guided LIV Selection for Adolescent Idiopathic Scoliosis

Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age10-18
SponsorThe Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Diagnosis of adolescent idiopathic scoliosis classified as Lenke type 1A or Lenke type 5C.

Age 10 to 18 years, inclusive.

Scheduled to undergo posterior spinal fusion using an all-pedicle screw instrumentation system.

Planned selective thoracic fusion or selective lumbar fusion, with a clinical need for lowest instrumented vertebra decision-making.

Disqualifiers

- Congenital scoliosis, neuromuscular scoliosis, syndromic scoliosis, or other non-idiopathic scoliosis.

History of spinal trauma, spinal tumor, spinal tuberculosis, or spinal infection.

Previous spinal surgery.

Severe sagittal spinal deformity, such as Scheuermann disease, for which the study model is not applicable.

Trial design

Design model

Parallel

Treatments tested in this trial

  • Drum Tower Rule Machine Learning-Guided Decision Support

    Other intervention

    A locked machine learning-based decision support model will be used before surgery to estimate the risk of postoperative distal adding-on and provide a recommendation for lowest instrumented vertebra selection. The model output will be available to surgeons in the model-guided group only.

  • Conventional LIV Decision-Making

    Other intervention

    The lowest instrumented vertebra will be selected by the surgeon according to routine clinical experience and existing surgical principles, without access to the Drum Tower Rule model output.

  • Posterior Spinal Fusion

    Procedure/Surgery

    All participants will undergo standard posterior spinal fusion using an all-pedicle screw instrumentation system.

Treatment groups

300 Participants
are divided into 2 treatment groups
Group A: Model-Guided GroupExperimental treatment 2 interventions
Group B: Conventional-Experience GroupActive comparator 2 interventions

Trial outcomes

Primary outcomes

1

Incidence of Distal Adding-on

Distal adding-on is defined as an increase of more than 5 degrees in the disc angle below the lowest instrumented vertebra compared with the immediate postoperative radiograph, or an increase of more than 5 mm in the translation of the vertebra below the lowest instrumented vertebra relative to the central sacral vertical line. The outcome will be assessed by blinded radiographic reviewers.

Time frame
24 months after surgery

Secondary outcomes

1

Scoliosis Research Society-22 Score

Health-related quality of life will be assessed using the Scoliosis Research Society-22 questionnaire.

Time frame
24 months after surgery

Other outcomes

1

Incidence of Surgery-Related Complications

Surgery-related complications include perioperative complications, instrumentation failure, proximal junctional kyphosis, adjacent segment degeneration, pseudarthrosis, infection, and revision surgery.

Time frame
From surgery to 24 months after surgery

Sponsors and contacts

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