Artificial Intelligence and Machine Learning in Reconstructive Microsurgery.

Area of Science:

• Surgical Technology
• Medical Artificial Intelligence
• Microsurgery Innovations

Background:

• Reconstructive microsurgery faces challenges in subjective assessments, operator dependency, and inconsistent monitoring.
• Artificial intelligence (AI) and machine learning (ML) offer data-driven solutions to improve surgical precision and workflow standardization.

Purpose of the Study:

• To explore the transformative impact of AI and ML on reconstructive microsurgery.
• To highlight AI applications across the perioperative continuum, from preoperative planning to postoperative monitoring and patient communication.

Main Methods:

• Review of contemporary AI/ML applications in reconstructive microsurgery.
• Analysis of AI performance in risk stratification, perforator localization (CNNs), intraoperative assistance (robotics), and postoperative monitoring (image analysis).
• Evaluation of AI's role in patient communication (visual simulation, LLMs).

Main Results:

• ML algorithms show high accuracy in predicting complications like flap loss.
• Convolutional Neural Networks (CNNs) achieve high Dice coefficients for perforator detection.
• AI-enhanced robotic platforms offer submillimeter precision in supermicrosurgery.
• AI systems accurately classify flap perfusion and detect vascular compromise.
• AI tools improve patient education and informed consent.

Conclusions:

• AI and ML significantly enhance precision, standardization, and outcomes in reconstructive microsurgery.
• Challenges include data quality, bias, and dataset imbalance, requiring explainable AI and collaboration.
• Thoughtful AI integration augments surgical expertise, improving patient care without replacing clinical judgment.