Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities

Surface topography systems enable the capture of spinal dynamic movement. A visualization of possible unique movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated a visualization approach using Siamese neural networks (SNN) and checked, if the identification of individuals is possible based on dynamic spinal data. The presented visualization approach seems promising in visualizing subjects in the presence of intraindividual variability between different gait cycles as well as day-to-day variability. Overall, the results indicate a possible existence of a personal spinal ‘fingerprint’. The work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases

Evaluation of 3D vertebral and pelvic position by surface topography in asymptomatic females: presentation of normative reference data

Background!#!Deviations from a conventional physiologic posture are often a cause of complaint. According to current literature, the upright physiological spine posture exhibits inclinations in the sagittal plane but not in the coronal and transverse planes, but individual vertebral body positions of asymptomatic adults have rarely been described using surface topography. Therefore, this work aims to form a normative reference dataset for the thoracic and lumbar vertebral bodies and for the pelvis in all three planes in asymptomatic women.!##!Methods!#!In a prospective, cross-sectional, monocentric study, 100 pain-free asymptomatic women, aged 20-64 years were enrolled. Habitual standing positions of the trunk were measured using surface topography. Data were analyzed in all three planes. Age sub-analysis was: 1) ages ≤ 40 years and 2) ages ≥ 41 years. Two-sample t-tests were used for age comparisons of the vertebral bodies, vertebra prominence (VP)-L4, and global parameters. One-sample t-tests were used to test deviations from symmetrical zero positions of VP-L4.!##!Results!#!Coronal plane: on average, the vertebral bodies were tilted to the right between the VP and T4 (maximum: T2 - 1.8° ± 3.2), while between T6 and T11 they were tilted to the left (maximum: T7 1.1° ± 1.9). T5 and L2 were in a neutral position, overall depicting a mean right-sided lateral flexion from T2 to T7 (apex at T5). Sagittal plane: the kyphotic apex resided at T8 with - 0.5° ± 3.6 and the lumbar lordotic apex at L3 with - 2.1° ± 7.4. Transverse plane: participants had a mean vertebral body rotation to the right ranging from T6 to L4 (maximum: T11 - 2.2° ± 3.5). Age-specific differences were seen in the sagittal plane and had little effect on overall posture.!##!Conclusions!#!Asymptomatic female volunteers standing in a habitual posture displayed an average vertebral rotation and lateral flexion to the right in vertebral segments T2-T7. The physiological asymmetrical posture of women could be considered in spinal therapies. With regard to spinal surgery, it should be clarified whether an approximation to an absolutely symmetrical posture is desirable from a biomechanical point of view? This data set can also be used as a reference in clinical practice.!##!Trial registration!#!This study was registered with WHO (INT: DRKS00010834) and approved by the responsible ethics committee at the Rhineland-Palatinate Medical Association (837.194.16)

Creation and Evaluation of a Severity Classification of Hyperkyphosis and Hypolordosis for Exercise Therapy

The rise in the occurrence of musculoskeletal disorders, such as thoracic hyperkyphosis (THK) or lumbar hypolordosis (LHL), is a result of demographic changes. Exercise therapy is an effective approach that can reduce related disabilities and costs. To ensure successful therapy, an individualized exercise program adapted to the severity of the disorder is expedient. Nevertheless, appropriate classification systems are scarce. This project aimed to develop and evaluate a severity classification focused on exercise therapy for patients with THK or LHL. A multilevel severity classification was developed and evaluated by means of an online survey. Reference limits of spinal shape angles were established by data from video rasterstereography of 201 healthy participants. A mean kyphosis angle of 50.03° and an average lordosis angle of 40.72° were calculated as healthy references. The strength of the multilevel classification consisting of the combination of subjective pain and objective spinal shape factors was confirmed by the survey (70% agreement). In particular, the included pain parameters were considered relevant by 78% of the experts. Even though the results of the survey provide important evidence for further analyses and optimization options of the classification system, the current version is still acceptable as therapeutic support

Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities

Surface topography systems enable the capture of spinal dynamic movement. A visualization of possible unique movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated a visualization approach using Siamese neural networks (SNN) and checked, if the identification of individuals is possible based on dynamic spinal data. The presented visualization approach seems promising in visualizing subjects in the presence of intraindividual variability between different gait cycles as well as day-to-day variability. Overall, the results indicate a possible existence of a personal spinal ‘fingerprint’. The work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases

Visualization of interindividual differences in spinal dynamics in the presence of intraindividual variabilities

Surface topography systems enable the capture of spinal dynamic movement. A visualization of possible unique movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated a visualization approach using Siamese neural networks (SNN) and checked, if the identification of individuals is possible based on dynamic spinal data. The presented visualization approach seems promising in visualizing subjects in the presence of intraindividual variability between different gait cycles as well as day-to-day variability. Overall, the results indicate a possible existence of a personal spinal ‘fingerprint’. The work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively

Table1_Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.pdf

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.</p