Machine Learning in Pain Medicine: An Up-To-Date Systematic Review
Maria Matsangidou, Andreas Liampas, Melpo Pittara, Constantinos S. Pattichi, Panagiotis Zis
Abstract
INTRODUCTION: Pain is the unpleasant sensation and emotional experience that leads to poor quality of life for millions of people worldwide. Considering the complexity in understanding the principles of pain and its significant impact on individuals and society, research focuses to deliver innovative pain relief methods and techniques. This review explores the clinical uses of machine learning (ML) for the diagnosis, classification, and management of pain. METHODS: A systematic review of the current literature was conducted using the PubMed database library. RESULTS: Twenty-six papers related to pain and ML research were included. Most of the studies used ML for effectively classifying the patients' level of pain, followed by use of ML for the prediction of manifestation of pain and for pain management. A less common reason for performing ML analysis was for the diagnosis of pain. The different approaches are thoroughly discussed. CONCLUSION: ML is increasingly used in pain medicine and appears to be more effective compared to traditional statistical approaches in the diagnosis, classification, and management of pain.