SpineSighter: an AI-driven approach for automatic classification of spinal function from video

Liu, Zebang, Hicks, Yulia ORCID: https://orcid.org/0000-0002-7179-4587 and Sheeran, Liba ORCID: https://orcid.org/0000-0002-1502-764X 2024. SpineSighter: an AI-driven approach for automatic classification of spinal function from video. Procedia Computer Science 246 , pp. 3977-3989. 10.1016/j.procs.2024.09.172

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Abstract

Low Back Pain (LBP) is a prevalent musculoskeletal disorder affecting over 80% of the population over their lifetime and is a leading cause of disability globally. The most frequent type, non-specific LBP (NSLBP) does not have a clearly identifiable pathology cause. Current clinical guidelines advocate for tailored management and self-care approaches for NSLBP. The effectiveness of these personalised management plans significantly depends on accurate and on-going assessment of the patient’s spinal function. This presents considerable challenges for both clinicians and patients. This study introduces “SpineSighter”, an artificial intelligence (AI) model developed to tailor management of NSLBP by categorising patients based on their spinal function either into High Function (HF) and Low Function (LF) subsets. Utilising standard video recordings and computer vision technology, SpineSighter analyses motion features such as angular displacement, velocity, and acceleration during repeated forward flexion tests. The model showed high accuracy in classifying spinal function, achieving an accuracy of 95.13%, sensitivity of 93.81%, specificity of 96.00%, and an F1 score of 0.9442. This innovative use of AI highlights the importance of velocity as a critical indicator of spinal functional differences, opening new avenues for personalised clinical management, self-care and recovery strategies of NSLBP.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Healthcare Sciences Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2024-09-17
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	3 December 2024
Last Modified:	16 Dec 2024 14:14
URI:	https://orca.cardiff.ac.uk/id/eprint/174462

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