Towards Data-Driven Reconstructive Surgery: Artificial Intelligence for Risk Prediction and Personalized Care

This thesis investigated the transformative potential of artificial intelligence (AI) and machine learning (ML) within the field of surgery, with a specific focus on enhancing predictive capabilities in plastic and reconstructive surgery. The core research presented in this thesis focused on the development and rigorous evaluation of multiple supervised ML models tailored to predict specific, high-impact adverse outcomes in diverse reconstructive settings. These studies successfully demonstrated that machine learning ML models could accurately predict adverse events following breast reconstruction, abdominal wall reconstruction (AWR), head and neck free flap reconstruction, and burn care. Across these varied applications, ML models consistently outperformed conventional logistic regression models developed on the same datasets, not only achieving superior predictive accuracy (measured by metrics like AUC) but also identifying a broader and more nuanced set of risk factors. A key contribution was the development of the Skin Graft Loss Risk Estimator, validated on a large national cohort and translated into a transparent, web-based clinical tool, demonstrating a clear pathway from complex AI to practical, interpretable decision support. The clinical implications of these findings are significant. Validated ML models offer the potential for highly individualized preoperative risk assessment, thereby enhancing patient counseling, facilitating more informed shared decision-making, guiding targeted preoperative optimization strategies, and potentially refining surgical planning and technique selection.

However, the successful translation of these AI tools into routine clinical practice necessitates careful consideration of implementation challenges and governance structures. This thesis incorporated a framework addressing the critical stages of AI-enabled clinical decision support system (CDSS) deployment. The Development stage emphasizes the importance of clear problem definition, rigorous data quality assessment, ensuring adequate infrastructure, making appropriate algorithm selections, and evaluating the net clinical benefit beyond mere statistical performance. Successful Implementation requires focusing on seamless clinical workflow integration, clear end-user definition, establishing long-term stewardship for ongoing model maintenance, ensuring stakeholder buy-in, defining meaningful success metrics, and proactively addressing potential workforce implications. Finally, the Regulation stage highlights the imperative to evaluate and mitigate algorithmic bias, ensure appropriate human oversight in the decision loop, navigate the complex trade-offs between model interpretability and predictive performance, establish clear accountability frameworks for model failures, and rigorously safeguard patient data privacy.

This thesis provides compelling evidence for the feasibility and utility of AI and ML in significantly advancing predictive analytics in plastic and reconstructive surgery. It contributes novel, validated predictive models for several key clinical scenarios and underscores the limitations of existing approaches. Critically, it also emphasizes that the successful and ethical integration of these powerful technologies into clinical care requires a deliberate, systematic approach guided by robust governance principles addressing development, implementation, and regulation. By embracing such a framework, the surgical community can responsibly leverage AI to augment clinical expertise, personalize patient care, and ultimately improve surgical outcomes.