Machine learning for discovery of clinical pain biomarkers following spinal cord injury.

Chronic pain is highly prevalent in patients with spinal cord injury (SCI) and further degrades the quality of life in individuals already struggling with somatic, motor, and autonomic deficits. The management of SCI pain is challenging, mainly due to the lack of reliable, FDA-approved diagnostics, effective therapies, and incomplete understanding of the underlying mechanisms. These limitations have led to increased efforts dedicated to the identification of objective pain biomarkers. However, the FDA has yet to approve a physiologically relevant biomarker for the assessment of pain in populations with SCI. Given the multidimensional nature of pain, there is increasing recognition that composite biomarkers are needed. In this paper, we review several candidate pain signatures and discuss how the inclusion of multi-modal features such as self-reported questionnaires and behavioural measures should also be considered in the identification of comprehensive biomarkers of SCI pain. Since multi-modal, large-scale data presents a particular computational challenge, we further argue that AI and ML approaches enable novel combinatorial designs of SCI pain biomarkers. The advantages of AI and ML methods, which continue to evolve at a rapid pace, include computational efficiency, discovery of latent or embedded patterns in complex data architectures, personalized diagnostics, and minimization of potential bias. We also caution against over-reliance on physiological or neural imaging features that ignore the demographic, motivational, emotional, cognitive and cultural dimensions of pain, while advocating for AI/ML models with improved interpretability.