Spinal Growth Modulation Using a Novel Intravertebral Epiphyseal Device in an Immature Porcine Model

PURPOSE: Fusionless growth modulation is an attractive alternative to conventional treatments of idiopathic scoliosis. To date, fusionless devices achieve unilateral growth modulation by compressing the intervertebral disc. This study explores a device to control spinal alignment and vertebral morphology via growth modulation while excluding the disc in a porcine model. METHODS: A device that locally encloses the vertebral growth plate exclusive of the disc was introduced anteriorly over T5–T8 in four immature pigs (experimental) while three underwent surgery without instrumentation (sham) and two were selected as controls. Bi-weekly coronal and lateral radiographs were taken over the 12-week follow-up to document vertebral morphology and spinal alignment modifications via an inverse approach (creation of deformity). RESULTS: All animals completed the experiment with no postoperative complications. Control and sham groups showed no significant changes in spinal alignment. Experimental group achieved a final coronal Cobb angle of 6.5° ± 3.5° (constrained to the four instrumented levels) and no alteration to the sagittal profile was observed. Solely the experimental group ended with consistent vertebral wedging of 4.1° ± 3.6° amounting to a cumulative wedging of up to 25° and a concurring difference in left/right vertebral height of 1.24 ± 1.86 mm in the coronal plane. CONCLUSIONS: The proposed intravertebral epiphyseal device, for the early treatment of progressive idiopathic scoliosis, demonstrated its feasibility by manipulating spinal alignment through the realization of local growth modulation exclusive of the intervertebral disc

Image-based modelling for Adolescent Idiopathic Scoliosis: Mechanistic machine learning analysis and prediction

Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS) is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. We propose a mechanistic machine learning algorithm in order to study patient-specific AIS curve progression, which is associated with the bone growth and other genetic and environmental factors. Two different frameworks are used to analyse and predict curve progression, one with implementing clinical data extracted from 2D X-ray images and the other one with incorporating both clinical data and physical equations governing the non-uniform bone growth. The physical equations governing bone growth are affiliated with calculating all stress components at each region. The stress values are evaluated through a surrogate finite element simulation and a bone growth model on a detailed patient-specific geometry of the human spine. We also propose a patient-specific framework to generate the volumetric model of human spine which is partitioned into different tissues for both vertebra and intervertebral disc. It is shown that implementing physical equations governing bone growth into the prediction framework will notably improve the prediction results as compared to only using clinical data for prediction. In addition, we can predict curve progression at ages outside the range of training samples

Image-based modelling for Adolescent Idiopathic Scoliosis: Mechanistic machine learning analysis and prediction

Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS) is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. We propose a mechanistic machine learning algorithm in order to study patient-specific AIS curve progression, which is associated with the bone growth and other genetic and environmental factors. Two different frameworks are used to analyse and predict curve progression, one with implementing clinical data extracted from 2D X-ray images and the other one with incorporating both clinical data and physical equations governing the non-uniform bone growth. The physical equations governing bone growth are affiliated with calculating all stress components at each region. The stress values are evaluated through a surrogate finite element simulation and a bone growth model on a detailed patient-specific geometry of the human spine. We also propose a patient-specific framework to generate the volumetric model of human spine which is partitioned into different tissues for both vertebra and intervertebral disc. It is shown that implementing physical equations governing bone growth into the prediction framework will notably improve the prediction results as compared to only using clinical data for prediction. In addition, we can predict curve progression at ages outside the range of training samples.Mechatronic Desig