BackTracker: Machine learning to identify kinematic phenotypes for personalised exercise management in non-specific low back pain.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 91.91% accuracy for backward-bend video analysis

• Primary Outcome: Diagnostic accuracy for identifying flexion vs extension pattern motor control impairments

• Nnt Or Sensitivity: Sensitivity and specificity not reported; AUC 0.9422

• Confidence Interval: 95% CI: 84.8-99.1%

Clinical Application

Requires video recording equipment and AI processing capabilities - implementation challenges in routine practice Could complement RACGP low back pain guidelines emphasising early active management. Would need TGA approval as diagnostic software. Cost-effectiveness unclear for PBS consideration Adults with non-specific low back pain requiring movement pattern assessment

Abstract

BACKGROUND: Low back pain (LBP) is a leading cause of global disability. Most cases are non-specific (NSLBP) and lack identifiable causes. Early active management is endorsed by clinical guidelines; however, exercises are rarely customised despite substantial variability in impairments. Existing classification systems can support targeted rehabilitation but require extensive clinical training and lengthy assessment procedures, limiting timely personalised care. OBJECTIVE: This study used AI methods to identify the two most common motor control impairments (MCIs)-flexion and extension patterns (FP/EP)-in NSLBP. The approach used spinal silhouettes extracted from movement videos to enable self-phenotyping and guide personalised exercise selection. METHODS: Data were collected from a research fellowship involving ninety NSLBP participants classified by an expert physiotherapist (LS) into FP or EP MCIs. Participants performed standard forward- and backward-bend tasks recorded in the sagittal plane. Pose estimation and instance segmentation techniques were applied to extract motion features and spine silhouettes. From each participant, a curated set of 80 black-and-white back images captured at specific bending angles was produced. These features were used to train a feedforward neural network. Model performance was assessed using five-fold cross-validation with accuracy, sensitivity, specificity, F1 score and AUC. RESULTS: The model achieved a diagnostic accuracy of 91.91% (95% CI 84.8-99.1) for backward-bend videos, exceeding reported inter-examiner agreement rates for trained physiotherapists. Robustness was supported by a mean AUC of 0.9422. Accuracy was lower for forward-bend images (86.69%), combined tasks (86.29%), or PROMs alone (63.82%). Adding PROMs to forward- or backward-bend tasks provided only modest improvements (66.32% and 71.62%, respectively). CONCLUSION: The model reliably distinguished between FP and EP NSLBP subgroups, demonstrating the potential of AI to support timely personalised rehabilitation. The integration of PROMs with motion features reduced classification accuracy, suggesting that self-reported outcomes may provide limited benefit when tailoring exercises to specific physical impairments.