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Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49 Page 302

Figure 2. AI-supported patient-specific processes in plastic surgery. AI: Artificial intelligence.

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readmissions [Table 1] . In a pilot study by Mavioso et al., the preoperative utility of ML was evaluated for
semi-automatic assessment of Angio CT imaging for forty patients scheduled for deep inferior epigastric
perforators (DIEP) breast reconstruction [Table 2]. Specifically, Mavioso et al. utilized a paired sample
[34]
t-test and Wilcoxon test to compare the blood vessel sizes determined using semi-automatic identification
against manual identification . Additionally, a one sample t-test was performed to evaluate the estimated
[34]
location of the blood vessels when utilizing semi-automatic identification . When compared to the manual
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procedure performed by the imaging team, ML analysis of vessel caliber, orientation, and location
significantly reduced the time spent on preoperative planning for DIEP flap reconstruction. However, the
software could not accurately estimate the caliber of small vessels (< 1.5 mm) . Additionally, the vertical
[34]
component of vessel location differed by 2-3 mm from the manual method, although this discrepancy did
not impact the dissection. Overall, this study demonstrates that ML may decrease the time spent on surgical
planning and simplify the overall process.

ML algorithms can also be valuable for the prompt detection of complications following breast surgery. By
analyzing available patient data, these algorithms can identify patterns and determine the associations
among relevant variables . Kiranantawat et al. developed the first smartphone application for microsurgery
[1]
monitoring by training the algorithm with photographic data of fingers undergoing venous or arterial
congestion . Across forty-two participants, the application successfully assessed the vascular status of
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fingers with a sensitivity and specificity of 94% and 98%, respectively . This study suggests that ML could
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enhance early detection of postoperative flap failure and help optimize monitoring of the flap after surgery.
Another study by Myung et al. developed an ML model to determine patient-specific characteristics and
surgical factors that lead to an increased risk of donor site complications after the performance of
abdominal flaps for breast reconstruction . After analyzing 568 patients, Myung et al. discovered that the
[36]