Author links open overlay panelAbstractBackgroundFlap training can be technically demanding with a steep learning curve. Pigs has long been employed as an animal model for flap training. This cadaveric study was designed to (1) investigate the anatomy of pig medial cubital flap (MCF) and how it could be utilized surgically, (2) compare human medial arm flap (MAF) and pig MCF, and (3) investigate the feasibility of using the lard-based vascular injection technique in cadaveric preparations for flap training.
MethodsThe vascular anatomy and surgical procedures of the MCF were conducted in fifteen (n = 15) and five lard-infused pig cadavers (n = 5), respectively. The primary parameters were the outer diameter and length of the pedicle of the MCF, in other words, the collateral ulnar artery (CUA). A comparison was made between the pig MCF and its human counterpart.
ResultsLard-infused samples exhibited satisfactory elasticity, and the perforator arteries could be successfully infused with lard and clearly observed. The CUA was evident in all 15 samples and exhibited several muscular branches and a skin perforator. The diameter and length of CUA were 1.41 ± 0.30 mm and 2.07 ± 0.35 cm, respectively. Pig MCFs were designed as an oval area on the medial side of the elbow joint and could be harvested on a proximal pedicle or as chimeric flaps containing muscle, MCAN and skin.
ConclusionMCFs and MAFs exhibited notable similarities in terms of anatomical locations, pedicles, and surgical procedures, clearly demonstrating the feasibility of using pigs as an animal model for the generation of MAFs and the potential use of such models in clinical implications: surgical training and anatomical research. The lard-based vascular injection technique makes cadaveric preparation easy and safe for subsequent cadaver surgical training and is worthy of further application.
KeywordsAnatomy
Animal model
Medial arm flap
Superior ulnar collateral artery
Reconstruction
Swine
View Abstract© 2025 Elsevier GmbH. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Comments (0)