Machine Learning in Pain Neuromodulation.
Journal:
Advances in experimental medicine and biology
PMID:
39523286
Abstract
This chapter highlights the intersection of pain neuromodulation and machine learning (ML), exploring current limitations in pain management and how ML techniques can address these challenges. Neuromodulation technologies, such as spinal cord stimulation (SCS), have emerged as promising interventions for chronic pain, but limitations such as patient selection have resulted in high rates of failure and costly removal of these devices. ML offers a powerful approach to augment pain management outcomes by leveraging predictive modeling for enhanced patient selection, adaptive algorithms for programming optimization, and identification of objective biomarkers for improved outcome assessment. This chapter discusses various ML applications in pain neuromodulation and how we can expect it to shape the future of the field. While ML holds great promise, challenges such as algorithm transparency, data quality, and generalizability must be addressed to fully realize its potential in revolutionizing pain management.