Machine Learning Algorithms for Predicting Cobb Angle Beyond 25° in Female Adolescent Idiopathic Scoliosis Patients

Retrospective cohort study.

To develop a machine learning (ML) model that predicts the progression of AIS using minimal radiographs and simple questionnaires during the first visit.

Several factors are associated with angle progression in patients with AIS. However, it is challenging to predict angular progression at the first visit.

Among female patients with AIS treated at a single institution from July 2011 to February 2023, 1119 cases were studied. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first and last visits. The last visit was defined differently based on treatment plans. For patients slated for surgery or bracing, the last visit occurred just before these interventions. For others, it was their final visit before turning 18 years. Angular progression was defined as a Cobb angle >25° for each of the proximal thoracic (PT), main thoracic (MT), and thoracolumbar/lumbar (TLL) curves at the last visit. ML algorithms were employed to develop individual binary classification models for each type of curve (PT, MT, and TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the area under the curve (AUC) and Recall scores. Feature importance was evaluated to understand the contribution of each feature to the model predictions.

For PT, MT, and TLL progression, the top-performing models exhibit AUC values of 0.94, 0.89, and 0.84 and achieve recall rates of 0.90, 0.85, and 0.81. The most significant factors predicting progression varied for each curve: initial Cobb angle for PT, presence of menarche for MT, and Risser grade for TLL.

This study introduces an ML-based model using simple data at the first visit to precisely predict angle progression in female patients with AIS.