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Page 136 Villavisanis et al. Art Int Surg. 2025;5:133-38 https://dx.doi.org/10.20517/ais.2024.89

POSTOPERATIVE MONITORING
Much of the discussion and research centered on artificial intelligence and machine learning in
microvascular reconstructive surgery has been dedicated to postoperative flap monitoring, given the
importance of early identification of postoperative complications and timely return to the operating
[32]
room .

A recent study in the JAMA network described the development of a cellphone-based application for
[33]
postoperative free flap monitoring . The authors leveraged artificial intelligence to develop models
sensitive to venous and arterial insufficiency, based on over 11,000 unique clinical photos . The models
[33]
were 97.5% sensitive in recognizing arterial insufficiency and 92.8% sensitive in recognizing venous
insufficiency based on clinical photographs alone (Kim et al. 2024). Such models may aid clinicians in the
early identification of flap failure and may be especially useful in regions, or units, typically naïve to
[33]
postoperative flap monitoring . This particular initiative may also encourage postoperative monitoring
with clinical photographs at regular postoperative intervals, which may allow clinicians to remotely monitor
free-flap postoperative progression .
[33]

Other groups have applied artificial intelligence methods to large datasets to analyze postoperative risk
factors for flap failure . Colleagues in Toronto conducted a clinical study of over one thousand patients
[34]
undergoing microvascular free flap breast reconstruction. Among the twelve patients who experienced flap
failure, the authors identified significant predictors including obesity and smoking . While these risk
[34]
factors have been previously described, the application of artificial intelligence to large datasets may aid
clinicians in predicting more nuanced outcomes for patient cohorts undergoing a diverse range of free flap
reconstruction. As additional data or images are accrued, artificial intelligence can be trained and
broadened to more accurately calculate risk or outcome occurrences.

FUTURE APPLICATIONS
Future endeavors should aim to build upon previously established work to expand the depth, breadth, and
accuracy of applications. This may involve the application of artificial intelligence to preoperative flap
imaging. With a predictive model, clinicians could envision artificial intelligence predicting and selecting
the most viable vascular perforators for a reconstructive flap; however, this type of data should be leveraged
in the context of patient-specific anatomy and surgeon experience. Intraoperatively, additional opportunity
exists for refining artificial intelligence-generated support of intraoperative decision making, which may be
especially useful in lower-resource settings or single-provider practice models. Augmented reality driven by
artificial intelligence could augment surgical dissection in a real-time manner to help identify critical
structures, vascular anatomy, or hazardous surgical maneuvers. Finally, postoperative monitoring may be
supported by systems leveraging artificial intelligence to aid in automating flap monitoring to generate
additional real-time data that may reduce the time from flap complication identification to return to the
operating room.

CONCLUSIONS
Artificial intelligence has had an undeniable impact on clinical medicine and surgery; within microvascular
free flap reconstruction, artificial intelligence continues to impact patient selection and prediction of
preoperative outcomes, intraoperative assessment, and postoperative monitoring. While artificial
intelligence will augment our ability to plan, implement, and monitor free flap reconstruction for our
patients, clinicians and surgeons should continue to rely on in-person physical examination to corroborate
data from emerging technology to yield the most optimal clinical outcomes. Based on the potential impact
and implications of this work to patients and clinicians alike, we believe future research in this arena to be a