Research Output
Finite element modelling of a synthetic paediatric spine for biomechanical investigation
Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review
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Publication
Studies on paediatric spines commonly use human adult or immature porcine spines as specimens, because it is difficult to obtain actual paediatric specimens. There are quite obvious differences, such as geometry, size, bone morphology, and orientation of facet joint for these specimens, compared to paediatric spine. Hence, development of synthetic models that can behave similarly to actual paediatric spines, particularly in term of range of motion (ROM), could provide a significant contribution for paediatric spine research. This study aims to develop a synthetic paediatric spine using finite element modelling and evaluate the reliability of the model by comparing it with the experimental data under certain load conditions. The ROM of the paediatric spine was measured using a validated FE model at ±0.5 Nm moment in order to determine the moment required by the synthetic spine to achieve the same ROM. The results showed that the synthetic spine required two moments, ±2 Nm for lateral-bending and axial rotation, and ±3 Nm for flexion-extension, to obtain the paediatric ROM. The synthetic spine was shown to be stiffer in flexion-extension but more flexible in lateral bending than the paediatric FE model, possibly as a result of the intervertebral disc’s simplified shape and the disc’s weak bonding with the vertebrae. Nevertheless, the synthetic paediatric spine has promising potential in the future as an alternative paediatric spine model for biomechanical investigation of paediatric cases.
Publication
Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.