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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.

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Author:Carlo DindorfORCiD, Jürgen KonradiORCiD, Claudia WolfORCiD, Bertram Taetz, Gabriele BleserORCiD, Janine Huthwelker, Friederike Werthmann, Eva Bartaguiz, Johanna Kniepert, Philipp Drees, Ulrich BetzORCiD, Michael FröhlichORCiD
URN (permanent link):urn:nbn:de:hbz:386-kluedo-66166
ISSN:1424-8220
Parent Title (English):Sensors
Publisher:MDPI
Document Type:Article
Language of publication:English
Publication Date:2021/09/21
Year of Publication:2021
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2021/10/12
Issue:2021, 21(18), 6323
Number of page:18
Source:doi.org/10.3390/s21186323
Faculties / Organisational entities:Fachbereich Sozialwissenschaften
DDC-Cassification:7 Künste und Unterhaltung, Architektur, Raumplanung / 796 Sport
Collections:Open-Access-Publikationsfonds
Licence (German):Creative Commons 4.0 - Namensnennung (CC BY 4.0)