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.

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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).

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