Validation of An Artificial Intelligence-Enabled Skin Perforator Segmentation Tool in Computer-Assisted Osteocutaneous Fibular Free Flap Harvest: A Clinical Trial

Computer-assisted surgery has revolutionized reconstruction with more efficient, accurate, and predictable surgery, as reported in our previous studies. Skin perforators are vessels that travel through muscles and septa to supply the skin. The identification of skin perforators is crucial for a safe fibula osteocutaneous free flap harvest with computer-assisted surgery. Different methods have been proposed in the past, each of which has its own limitations.

Traditionally, skin perforators are identified with a Doppler ultrasound. Berrone et al. measured the locations with a Doppler ultrasound and imported the information back to guide virtual surgical planning. However, their study showed imprecise concordance between handheld Doppler measurements and the actual perforator locations; good correlation between the location of perforators and bone segments was identified in only four out of six cases investigated. To improve on the accuracy, computed tomography angiography was used for skin perforator identification. Battaglia et al. manually marked the perforating vessel location at the subcutaneous level and reported good correlation. However, the manual segmentation of the perforator was at the subcutaneous level only. The course of the perforators, which would be more significant for the design of computer-assisted fibula osteocutaneous free flap harvest, was not shown.

To incorporate the course of skin perforators into fibula osteocutaneous free flap virtual surgical planning, Ettinger et al. first described the technique of manual tracing from computed tomography angiography in 2018 and validated its accuracy in 2022. The median absolute difference between the computed tomography angiography and intraoperative measurements was 3 millimeters. However, reports quoted an average of 2 to 3 hours spent on tracing and modeling the course of the perforators depending on their number and anatomy; consequently, this adds a significant burden to healthcare professionals.

Recently, United Imaging Intelligence has developed an artificial intelligence-based program that offers a potential solution for accurate and efficient localization of skin perforators to be incorporated into the current virtual surgical planning workflow. The proposed study aims to validate its performance in a prospective case series. This will be the first study to investigate the use of an artificial intelligence-enabled program for fibula skin paddle perforator identification.