Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study.
Journal:
BMJ open
PMID:
36764713
Abstract
INTRODUCTION: The effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and 94%. Adequate patient selection is thought to be essential in reaching satisfactory results. However, no clear consensus has been reached on which factors are most predictive of successful surgery. A clinical decision tool that encompassed all aspects is still to be made. Artificial intelligence (AI) and machine learning algorithms use complex self-learning models that can be used to make patient-specific decision-making tools. The aim of this study is to develop and train an algorithm that can be used as an online available clinical prediction tool, to predict the risk of retear in patients undergoing rotator cuff repair.