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Quantification of Variations in Thoracic Vertebral Morphology & Curve Progression Prediction in AIS
Start Date: 6/8/2022Start Time: 12:00 PM
End Date: 6/8/2022End Time: 2:00 PM

Event Description
BIOMED PhD Thesis Defense

Title:
Quantification of Variations in Thoracic Vertebral Morphology and Prediction of Curve Progression and Correction in Adolescent Idiopathic Scoliosis (AIS)

Speaker:
Ausilah Alfraihat, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Sriram Balasubramanian, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
Adolescent Idiopathic Scoliosis (AIS) is a complex, three-dimensional (3D), spinal deformity of unknown etiology. Asymmetric loading modulates vertebral growth rates and promotes the progression of spinal deformity. AIS progression is closely related to growth spurt and timely intervention is necessary to restore normal spinal growth. Therefore, it is important to understand the effects of rapid skeletal growth on curve progression. However, there are no data on longitudinal vertebral growth patterns of the normative spine, and the alterations in these growth patterns in the scoliotic spine.
A major concern in managing AIS patients with a minor curvature is identifying which curves will progress to moderate or severe deformities that will require surgical intervention. The prediction of curve progression is important for the selection and timing of treatment. As the progression of scoliosis deformity is primarily dependent on remaining growth during skeletal immaturity, predicting the risk of curve progression relies on surrogate indicators. Previous studies have associated various clinical and radiographic factors such as gender, age, curve magnitude, and skeletal maturity with curve progression. However, attempts to use each of these features individually as a prognostic factor for curve progression showed inaccurate predictions. Uncertainty regarding curve progression might cause unnecessary treatments, repetitive radiation exposure, and increased cost of care related to follow-up visits. Although there is a consensus in the literature regarding prognostic factors associated with curve progression, the order of importance, as well as the combination of factors that are most predictive of curve progression, are unknown.

Anterior Vertebral Body Tethering (AVBT), a recent growth modulation technique, produces curve correction by harnessing a patient’s remaining growth. Despite the promising potential that AVBT holds in correcting curve deformity, precise indications and the patient-specific constructs required based on a patient’s clinical and radiographic parameters are unclear. Currently, indications for AVBT are based on the magnitude of the curve and more importantly the remaining skeletal growth in the patient. Skeletal growth remaining, a parameter that AVBT entirely relies on to correct the deformity, is not precisely predictable. Hence, the indications for anterior spinal tethering procedures remain controversial and AVBT surgical outcomes are not reliably predictable.

This thesis quantifies the variation in longitudinal growth patterns of thoracic vertebral body heights in skeletally normative and scoliotic rabbits. The scoliotic rabbits underwent unilateral rib tether surgery on the left side ribs to create a convexity on the right side. While these rabbits were growing their computed tomography scans were obtained at four-time points. Moreover, machine learning selection method was used to select the most predictive clinical and radiological features of adolescent idiopathic scoliosis curve progression and curve correction following anterior vertebral body tethering surgery. These selected features were used to train and test predictive machine learning models. In addition, a tool for AIS curve progression prediction was developed and made publicly available.
Contact Information:
Name: Natalia Broz
Email: njb33@drexel.edu
Ausilah Alfraihat
Location:
Remote
Audience:
  • Undergraduate Students
  • Graduate Students
  • Faculty
  • Staff

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