Artificial Intelligence and Machine Learning in Reconstructive Microsurgery

Ta-Chun Lin; Hsi-An Yang; Ren-Wen Huang; Cheng-Hung Lin

doi:10.1055/s-0045-1810062

Seminars in Plastic Surgery, Table of Contents

Semin Plast Surg 2025; 39(03): 190-198
DOI: 10.1055/s-0045-1810062

Review Article

Artificial Intelligence and Machine Learning in Reconstructive Microsurgery

Ta-Chun Lin

¹Department of Plastic and Reconstructive Surgery, Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University, Taoyuan, Taiwan

,

Hsi-An Yang

¹Department of Plastic and Reconstructive Surgery, Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University, Taoyuan, Taiwan

,

Ren-Wen Huang

¹Department of Plastic and Reconstructive Surgery, Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University, Taoyuan, Taiwan

,

Cheng-Hung Lin

¹Department of Plastic and Reconstructive Surgery, Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University, Taoyuan, Taiwan

› Author Affiliations

Abstract

Artificial intelligence (AI) and machine learning (ML) technologies are transforming reconstructive microsurgery through data-driven approaches that enhance precision and standardize clinical workflows. These innovations address long-standing challenges, including subjective assessment methodologies, operator-dependent decision-making, and inconsistent monitoring protocols across the perioperative continuum. Contemporary applications demonstrate remarkable capabilities in preoperative risk stratification, with ML algorithms achieving high predictive accuracy for complications such as flap loss and donor site morbidity. CNNs have revolutionized perforator localization, with advanced models achieving Dice coefficients of 91.87% in anatomical structure detection from CT angiography. Intraoperative assistance through AI-enhanced robotic platforms provides submillimeter precision and tremor filtration, particularly beneficial in supermicrosurgery involving vessels measuring 0.3- to 0.8-mm diameter. Postoperative monitoring represents a particularly promising domain, where AI-based image analysis systems achieve 98.4% accuracy in classifying flap perfusion status and detecting early vascular compromise. Automated platforms may enable continuous surveillance with reduced clinical workload while maintaining superior consistency compared with traditional subjective methods. Patient communication benefits from AI-driven visual simulation and large language models (LLMs) that generate personalized educational materials, enhancing informed consent processes. Critical implementation challenges include data quality, algorithmic bias, and inherent dataset imbalance, where complications represent rare but clinically crucial events. Future advancement requires explainable AI systems, multi-institutional collaboration, and comprehensive regulatory frameworks. When thoughtfully integrated, AI serves as a powerful augmentation tool that elevates microsurgical precision and outcomes while preserving the fundamental importance of surgical expertise and clinical judgment.

Keywords

artificial intelligence - machine learning - reconstructive microsurgery - flap monitoring

Full Text

References

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