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Page 134 Villavisanis et al. Art Int Surg. 2025;5:133-38 https://dx.doi.org/10.20517/ais.2024.89

free flap reconstruction. In the preoperative period, this includes physical examination of patient anatomy,
[3]
including close evaluation of donor site fitness , recipient site reconstructive need, and patient past medical
[4]
history . During the intra- and postoperative periods, clinical monitoring primarily relies on physical
examination, including serial arterial and venous signal evaluations, as well as clinical characteristics, such
as flap color, capillary refill, temperature, and caliber.

The advent of artificial intelligence has yielded improvements in clinical medicine , including but not
[5]
limited to genetics and personalized medicine , cardiovascular health , and pulmonary/critical care .
[8]
[6]
[7]
Within surgical subspecialties, artificial intelligence has been applied to clinical outcomes in orthopedic
surgery , vascular surgery , plastic surgery , and craniofacial surgery [12-14] .
[10]
[11]
[9]
In the present paper, the authors aim to review the salient literature and studies regarding the applications
of artificial intelligence to augment planning, assessment, and monitoring for free flap microvascular
reconstruction [Table 1]. While this review aims to provide a broad overview of artificial intelligence
applications across microvascular reconstruction, it should be noted the studies highlighted have nuances
that render results more or less applicable to specific anatomic regions, types of procedures, and certain
subpopulations of patients. Despite the nuances or focuses of each article, the generalizable principles of
these studies provide a future avenue for broader artificial intelligence research and investigation.

PREOPERATIVE PLANNING
Preoperative planning has played an increasingly important role in free flap microvascular
[15]
reconstruction . Factors including patient selection, donor site fitness, and recipient site evaluation
continue to play an essential role in overall success and outcomes . The advent of artificial intelligence has
[16]
provided a welcome opportunity for further evaluation of factors most important in preoperative planning
and predicting overall outcomes.

Vascular mapping has become increasingly employed prior to flap dissection and reconstruction, and this
has been an area fit for applications with artificial intelligence [17-19] . A recent study by Lim et al. focused on
the accuracy of artificial intelligence models in evaluating computed tomographic angiography (CTA) data
for preoperative flap planning, and the team assessed the ability of different large language models to
evaluate these preoperative CTA data for DIEP flap planning . When attending plastic surgeons assessed
[20]
AI-generated CTA evaluations, they found that these models could provide a general summary of relevant
CTA data but lacked important nuances that the surgical team desired for preoperative planning . This
[20]
can be applied for preoperative evaluation and flap selection, which is dependent on patient-specific factors
and defects for reconstruction. There may also be applications for determining flap volume appropriateness
for defect reconstruction, though this has not yet been described in any current studies.

Other studies aim to apply artificial intelligence to clinical datasets to predict overall outcomes following
flap microvascular reconstruction. Within head and neck surgery , machine learning successfully
[21]
leveraged patient characteristics to predict flap complications and loss . Models predicting total flap loss
[22]
exhibited accuracy of 0.63 to 0.98, with significant identified factors including gender, smoking status, use of
vein graft, hypertension, and laryngectomy . Similar machine learning models have created decision trees
[22]
[23]
to predict free flap complications based on preoperative demographics . Such research has important
[22]
applications to preoperative patient selection, family counseling, and clinical shared decision making .
Moreover, the trainability of predictive machine learning models allows for generative growth and accuracy
improvement over time. Another systematic review study protocol describes a study in process that aims to
summarize artificial intelligence applications in predicting flap outcomes .
[24]