ArticleBMJ open2023
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.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning-based prediction of retear after arthroscopic rotator cuff repair: a multicenter cohort study.JSES international · 2026Article
- Update of a prediction model for postoperative shoulder stiffness after arthroscopic rotator cuff repair.Communications medicine · 2025Article
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence.Journal of orthopaedics · 2024Review
- Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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. METHODS AND ANALYSIS: This is a retrospective, multicentre, cohort study using pooled individual patient data from multiple studies of patients who have undergone rotator cuff repair and were evaluated by advanced imaging for healing at a minimum of 6 months after surgery. This study consists of two parts. Part one: collecting all potential factors that might influence retear risks from retrospective multicentre data, aiming to include more than 1000 patients worldwide. Part two: combining all influencing factors into a model that can clinically be used as a prediction tool using machine learning. ETHICS AND DISSEMINATION: For safe multicentre data exchange and analysis, our Machine Learning Consortium adheres to the WHO regulation 'Policy on Use and Sharing of Data Collected by WHO in Member States Outside the Context of Public Health Emergencies'. The study results will be disseminated through publication in a peer-reviewed journal. Institutional Review Board approval does not apply to the current study protocol.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.