Artificial intelligence-based biomechanical models for predicting postoperative pain: A retrospective cohort analysis of clinical features before and after anesthesia
Abstract
Background: Most surgical patients experience moderate to severe pain, which makes postoperative pain management a challenge in healthcare. Traditional approaches to managing pain are often not successful since they do not take into account individual differences along with multifaceted pain mechanisms. Objective: The aim of this study is to develop and validate an artificial intelligence-based biomechanical model which aids in predicting postoperative pain patterns by utilising pre-anesthetic and post-anesthetic clinical features. Methods: In this retrospective cohort study, 324 elective orthopedic surgery patients were analysed between January 2020 and December 2023. This study made use of an integrative AI model catering to biomechanical parameters alongside anesthesia features and clinical parameters. Biomechanical modelling and model evaluation comprised deep learning architectures with cross-validation methods alongside conventional machine learning methods as well. Results: Traditional algorithms were significantly outperformed internally to an absolute value accuracy of 93.7% (p < 0.001). Age and socio-economic factors took the lead predictive model and together comprised 63.9% of the outcome variance, with the influence of the former being more than the latter. There was strong generalisation between the performance mean values of training and validation of delta margin of <0.05. Conclusion: AI-aided clinical features alongside a biomechanical model can clearly aid in predicting a patient’s postoperative pain pattern. Not only does this mindset centre around pain relief, it can also help and be effective in tailoring pain management techniques and have an impact on patient outcomes in a clinical environment.
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