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Allam et al. Plast Aesthet Res 2024;11:19 https://dx.doi.org/10.20517/2347-9264.2024.21 Page 5 of 16

the DIEA perforated the anterior fascia matched perfectly with the virtual overlays. The time required for
perforator tracking with AR was comparable to Doppler examinations, underscoring AR's potential utility
[12]
in surgical planning .

Assessing AR's clinical utility, Hummelink et al. found that AR projections [Figure 2B] significantly reduced
operative time and improved the accuracy of perforator identification compared to Doppler ultrasound .
[17]
The study reported a reduction of over 17 min for perforator mapping and 19 min in flap harvesting time in
the AR group. Additionally, the AR model correctly identified 61.7% of perforators, compared to 41.2% in
[17]
the Doppler group. However, the study lacked sufficient power to detect differences in complication rates .
Current research demonstrates the proof-of-concept and non-inferiority of AR-assisted perforator
identification and selection in autologous breast reconstruction [12,16,17] . Future studies are imperative to
further explore AR's impact on clinical outcomes and assess its cost-effectiveness for broader
implementation in routine breast microsurgical practice.

Artificial intelligence
Artificial Intelligence (AI) in surgical planning is a rapidly evolving and promising technological
advancement in breast reconstruction. One example of its powerful application is its use in preoperative
imaging. Given that imaging analysis for perforator selection is both labor-intensive and time-consuming,
integrating AI and machine learning processes such as convolutional or deep neural networks into this
process presents a viable solution. This integration could enhance time and cost efficiency while minimizing
observer bias and human error in image interpretation.

Traditionally, MRA and CTA analyses for perforator selection rely heavily on the expertise of technicians
and radiologists. The incorporation of AI into this process could streamline preoperative planning and
reduce the likelihood of error. A prospective study involving 40 patients undergoing DIEP flap
reconstruction demonstrated that semi-automatic identification of perforators from CTA images, facilitated
by computer vision AI and machine learning, saved approximately two h of preoperative planning time
compared to manual analysis . While the number of localized perforators was similar in both approaches,
[18]
the AI technique showed reduced error in perforators larger than 1.5 mm in caliber. However, it also
indicated a slight increase in error for smaller perforators, though these discrepancies were not clinically
significant and did not impact surgical technique .
[18]
The utility of AI extends beyond perforator selection, offering the potential for automated, preemptive
screening of patients for significant postoperative complications. Flap failure, while rare, is a significant
complication of autologous breast reconstruction and is challenging to predict due to the absence of clear
individual risk factors . O’Neill et al. developed a multifactorial ML algorithm that stratifies various patient
[19]
and treatment factors into risk categories for flap failure. Analyzing a retrospective cohort of 1,012 DIEP
flap breast reconstructions, the model identified four high-risk subgroups based on BMI, comorbidities, and
surgical history with high specificity for predicting flap failure . While the study size was smaller than
[19]
typically recommended for high predictive power in ML, resulting in lower-than-expected sensitivity, it lays
the groundwork for future research in applications of AI . This represents a proof-of-concept for an ML
[19]
program that could screen high-risk patients for flap failure, which in turn could allow surgeons to
pre-emptively consider alternative reconstruction options.

Looking ahead, with adequate preoperative and postoperative data, the combination of established methods
with ML and computer vision AI could be harnessed to analyze the characteristics of perforators and