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Validity of flexicurve and motion capture for measurements of thoracic kyphosis vs standing radiographic measurements

ABSTRACT: Abstract Thoracic kyphosis varies among healthy adults and typically increases with age. Excessive kyphosis (hyperkyphosis) is associated with negative health. Spinal alignment also affects spine loading, with implications for conditions such as vertebral fractures and back pain. Valid measurements of kyphosis are necessary for clinical and research assessment of age?related posture changes, and to support improved biomechanical understating of spine conditions. Independent validation of non?radiographic techniques, however, remains limited. The goal of this study was to compare standing radiographic kyphosis measurements with non?radiographic measurements and predictions of thoracic kyphosis using flexicurve and motion analysis markers, in order to determine their validity. Thirteen non?radiographic measures of thoracic kyphosis were obtained in each of 40 adult subjects who also underwent standing radiographs of the thoracic spine. Measures included estimates derived by fitting of polynomials or circles to the non?radiographic data, as well as predictions calculated using previously published methods. Intra?class correlations (ICC) and root?mean square errors (RMSEs) were calculated between radiographic and non?radiographic measures to determine validity. Most non?radiographic estimates of kyphosis show similar, weak to moderate levels of validity when compared to radiographic measurements, and RMSEs ranging from 8.0° to 20.8°. Unbiased estimates of radiographic measurements with moderate to good ICCs were identified, however, based on marker measurements, and new prediction equations were created with similar validity that also account for age and body habitus. Clinical significance: These non?radiographic measurements of thoracic kyphosis can be applied to clinical practice or to clinical studies with recognition of specific limitations. Independent validation of non?radiographic measurements of thoracic kyphosis remains limited. In this study, flexicurve and motion?analysis based measurements were validated versus standing radiographic measurements of thoracic kyphosis in a sample of adults aged 19 ? 93. Unbiased measurements based on motion analysis with moderate to good validity were identified, and new prediction equations were created with similar validity that also account for age and body habitus.

SUBMITTER: Grindle D

PROVIDER: S-EPMC7524230 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Project description:BackgroundMenopause would seem to exist as a period of accelerated changes for women and their upper torso mechanics. Whether these anthropometric changes reflect changes in pain states remains unclear. Plausible mechanisms of pain exist for the independent and combined effect of increasing breast size and thoracic kyphosis. Bra fit has the potential to change when the anthropometric measures (chest circumference and bust circumference) used to determine bra size change, such as postmenopausally.Identifying an association between breast size, thoracic kyphosis and thoracic spine pain in postmenopausal women and identifying the relevance of bra fit to this association may be of importance to the future management and education of post-menopausal women presenting clinically with thoracic spine pain.MethodsA cross-sectional study design. Fifty-one postmenopausal bra-wearing women were recruited. Measures included breast size (Triumph International), thoracic kyphosis (flexible curve), bra fitted (Y/N) and pain (Short Form McGill Pain Questionnaire) and tenderness on palpation (posteroanterior pressure testing). These measures were collected in one session at a physiotherapy clinic.ResultsThe majority of the women in this study were overweight or obese and wearing an incorrect sized bra. Pain was significantly related to breast size, body weight and BMI at mid thoracic levels (T7-8). In contrast self-reported thoracic pain was not correlated with age or index of kyphosis (thoracic kyphosis). Women with thoracic pain were no more likely to have their bra professionally fitted whereas women with a higher BMI and larger breasts were more likely to have their bra professionally fitted.ConclusionThe findings of this study show that larger breasts and increased BMI are associated with thoracic pain in postmenopausal women. This is unrelated to thoracic kyphosis. Increasing breast size and how a bra is worn may have biomechanical implications for the loaded thoracic spine and surrounding musculature. Post-menopause women present with a spectrum of anthropometrical changes that have the potential to contribute to altered biomechanics and affect pain states in the thoracic spine.

Project description:Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.

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Validity of flexicurve and motion capture for measurements of thoracic kyphosis vs standing radiographic measurements

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