Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.
Wearable inertial measurement units deep learning model detected adolescent idiopathic scoliosis with 96.59% accuracy.
Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.
The study's objective is to propose a novel non-invasive method for rapid screening and regular assessment of adolescent idiopathic scoliosis (adolescent idiopathic scoliosis) through development of a wearable system integrated with multiple inertial measurement units (inertial measurement units) and deep learning models.
Gait kinematic data were acquired from 124 participants (104 patients with average Cobb angle of 21.62 ± 7.93° and 20 healthy subjects) using a 9-inertial measurement units wearable device.
Scapular kinematics emerged as the most prominent marker of asymmetry, and knee joint kinematics served as the strongest indicator of severity.
Meanwhile, angular features of knee, hip and ankle joints demonstrated weak negative linear correlations with Cobb angle.
For Cobb angle prediction, the CNN-Transformer model regularized with Gaussian noise during training proved most effective, yielding a mean absolute error of 2.14 ± 0.28° and R 2 value of 0.85 ± 0.03, outperforming other architectural alternatives evaluated in this study.
Kinematic analysis of angular data validated the efficacy of the wearable system and effectively captured gait characteristics specific to adolescent idiopathic scoliosis.
The deep learning models accurately distinguished scoliosis patients from healthy cases and predicted Cobb angles using temporal kinematic angular sequences, providing a safe, non-invasive, operator-friendly approach suitable for rapid screening and regular assessment.