This paper is a systematic review protocol outlining the planned methodology to evaluate artificial intelligence for predicting flap surgery outcomes, and contains no clinical results.
Systematic Review
Do machine learning models improve the prediction of outcomes in adult patients undergoing flap surgery compared to clinical monitoring alone?
This protocol outlines a planned systematic review to evaluate the accuracy and effectiveness of artificial intelligence models in predicting outcomes and complications following flap surgery.
Introduction: Free flap surgery encompasses reconstruction of diverse tissue defects. Flap failure and complications such as infection and ischemia remain a concern following flap surgery, with the current post-operative standard of care being frequent bedside monitoring. Artificial intelligence such as machine learning models could help support surgeons in postoperative monitoring and predicting complications. The purpose of this systematic review is to provide the framework for a review analyzing the existing literature behind the use of artificial intelligence in assessing flap surgery outcomes and predicting postoperative complications. Methods: A systematic review will be conducted using EMBASE and MEDLINE (1974 to October 2021) to identify relevant literature. This will include studies investigating Artificial Intelligence and machine learning models used in the postoperative setting of flap surgery. Primary outcomes will include evaluating the accuracy of evaluating outcomes following flap surgery based on these models, including: flap success, healing and complications up to 1 month following surgery. Secondary outcomes include the analysis of benefits and drawbacks of using machine learning models for outcomes following flap surgery. Studies will be screened by two independent reviewers; risk of bias will be assessed using the Cochrane risk of bias tool with methodological quality assessed using the QUADAS-2 tool. Discussion: This protocol will provide the framework for a review summarizing the current literature exploring the role of Artificial Intelligence for flap surgery outcomes. Results will help provide surgeons with an overview of current applications and identify areas of potential further research and development. Conclusion: As current clinical practice is regular bedside monitoring, integrating Artificial Intelligence could make the process more efficient, accurate and safer for patients and reduce labour burden or healthcare system costs. This review can help identify areas of potential and improvement which could further aid achieving successful outcomes following flap surgery.
Moosa et al. (Wed,) conducted a systematic review in Flap surgery. Artificial Intelligence and Machine Learning models vs. Clinical monitoring alone was evaluated on Accuracy of evaluating outcomes and effectiveness in predicting outcomes following flap surgery. This paper is a systematic review protocol outlining the planned methodology to evaluate artificial intelligence for predicting flap surgery outcomes, and contains no clinical results.
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