Machine learning in risk assessment for microvascular head and neck surgery.

Abstract

Purpose: The integration of machine learning (ML) into microvascular surgery for the head and neck offers significant potential to enhance risk stratification, outcome prediction, and decision support. Traditional risk assessment methods are often limited in addressing the dynamic complexity of surgical outcomes. ML can analyze preoperative, intraoperative, and postoperative data to optimize patient management, minimize complications, and improve both functional and aesthetic results.

Results: ML has demonstrated potential in several key areas of microvascular surgery. It can be used for risk stratification by assessing preoperative patient data to predict complications such as flap failure or infections. Outcome prediction models, trained on large datasets, provide estimations of functional and cosmetic results, helping surgeons set realistic expectations for patients. ML-driven decision support systems assist in flap selection by considering anatomical and patient-specific factors.

Conclusion: Despite its potential, ML adoption in microvascular surgery faces challenges, including the need for high-quality annotated datasets, interpretability issues, and ethical concerns such as data privacy and algorithmic bias. To fully leverage ML's capabilities, standardized datasets, interpretable models, and seamless clinical integration are necessary. With further research and implementation, ML has the potential to revolutionize risk assessment in microvascular head and neck surgery, improving patient outcomes and surgical precision.