Development of a novel non-invasive digital clinical assessment tool and its 3D measurable parameters for the orthotic treatment of adolescent idiopathic scoliosis

Student thesis: Doctoral Thesis

Abstract

Adolescent Idiopathic Scoliosis (AIS) is the most common of the types of idiopathic scoliosis (IS), which has three-dimensional (3D) deformities which occur in the vertebrae, spine and rib cage during the adolescent period. The aetiology is still unknown but it is hypothesized to be multi-factorial. Orthotic treatment is the most commonly used conservative treatment for AIS to prevent curve progression and surgical intervention. Among the three pathological features of AIS, the coronal misalignment pattern is the most studied area because the two-dimensional (2D) coronal PA radiographic image is the most utilized as the primary clinical assessment tool and employed for both diagnosis and outcome measurement of AIS deformities in scoliosis clinics. The sagittal misalignment patterns have been the most unknown area of study. Lack of available comprehensive knowledge about the deformities of AIS in 3D space may leave clinicians to make assumptions in treating AIS. This may influence the success rates of orthotic treatment which have been inconsistent and found to vary considerably. The literature review and the survey project of this thesis also showed that there are not enough universally agreed-upon principles to guide orthotists in the 3D orthotic treatment of AIS, which may also induce significant quality deviations for each scoliosis orthotic device system.To address this clinical gap, it was necessary to develop new effective 3D surface level measurable parameters to quantify the 3D AIS deformities and spinal misalignment, and to build a new suitable 3D non-invasive digital assessment tool that can be used with the parameters to evaluate and improve 3D spinal biomechanical knowledge and correction for effective orthotic treatment.For this thesis, two spinal alignment measurable parameter systems were developed. First, a radiographic spinal alignment parameters (RSAPs) system was developed based on the 3D osseous structural characteristics of a human’s erect spine and the unique features in each spinal bony structural segment. The newly developed RSAPs were studied to determine if they are useful in quantifying coronal and sagittal AIS misalignment, especially for sagittal misalignment patterns, which had not been clearly defined prior to this study. Then the skin level spinal alignment parameters (SSAPs) were developed based on the previously established radiographic parameters (RSAPs), and validated by defining whether SSAPs correspond to RSAPs for the same subject and identifying if the skin profile of SSAPs accurately reflects the structural characteristics of a human’s erect spine. The 3D concept of skin level parameters (3DSPs) was developed by adding six key non-spinal alignment parameters to comprehensively quantify not only 3D global spinal misalignments of AIS but also detect other major skeletal scoliosis deformities from the skin surface level. In addition, the reference range of 3DSPs were defined and its discriminative validity was examined by comparing the values between the AIS group and the non-scoliosis group.This thesis introduced the development of a non-invasive digital calculating and visualisation assessment application by utilizing motion capture technology. This application can measure the corrective forces applied by corrective pads and provide immediate visual feedback on the computer screen by displaying the spinal misalignment and deformities numerically and visually, appearing in one of two colours depending on whether each value of 3DSPs is within the non-scoliotic reference range for that parameter while applying corrective forces. The feasibility of the newly developed assessment tool and measurable parameters was evaluated by showing their capability in quantifying 3D misalignment and deformities of AIS, by identifying the unclear 3D characteristics of AIS deformity, and by finding answers
Date of Award1 Feb 2018
LanguageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorPhilip Rowe (Supervisor) & (Supervisor)

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