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

Carlo Dindorf, Jürgen Konradi, Claudia Wolf, Bertram Taetz, Gabriele Bleser, Janine Huthwelker, Friederike Werthmann, Eva Bartaguiz, Johanna Kniepert, Philipp Drees, Ulrich Betz, Michael Fröhlich, Carlo Dindorf, Jürgen Konradi, Claudia Wolf, Bertram Taetz, Gabriele Bleser, Janine Huthwelker, Friederike Werthmann, Eva Bartaguiz, Johanna Kniepert, Philipp Drees, Ulrich Betz, Michael Fröhlich

Abstract

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.

Keywords: artificial intelligence; back pain; biomechanics; data mining; explainable artificial intelligence; machine learning; osteoarthritis; spinal fusion; spine.

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Exemplary posture of one correctly (3459598) and one falsely (8232865) classified subject. Bottom: Displayed LIME values show the effect for the 10 most important features. Negative values represent an effect toward the group of healthy subjects, with positive values indicating an effect that indicates an outlier (patient). Top: Vertebral body positions in the transversal (rotation), coronal (lateral flexion), and sagittal (flexion extension) planes. Positive values indicate a rotation/tilt to the left or ventral (toward flexion), while negative values indicate a rotation/tilt to the right or dorsal (toward extension). Blue = mean and standard deviation (SD) of healthy reference group; orange = mean and SD of group of subjects with the respective pathology; black = mean and SD of the 10 measurements of the subjects of interest.

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