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Published on: October 17, 2025
A minimal-incision and minimal-soft-tissue-injury (MISI) technique for robot-assisted total knee arthroplasty: a
Ho-Jung Jung1, Jonggu Shin2, Jong-Keun Kim3
1Department of Orthopaedic Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, Republic of Korea.
Background:
Robot-assisted total knee arthroplasty (TKA) with functional alignment enables precise bone cuts and soft-tissue preservation; however, array placement often requires additional or extended incisions, which may increase surgical invasiveness. To address these limitations, we developed the Minimal-Incision and Minimal-Soft-Tissue-Injury (MISI) technique, combining robotic precision with true minimally invasive principles.
Methods:
The MISI technique utilizes an approximately 10-cm midline incision with a mini-medial parapatellar approach. It allows secure array fixation through the primary incision while applying functional alignment principles to preserve the soft-tissue envelope. Femoral pins are inserted intra-incisionally, and tibial fixation employs a hybrid approach: one intra-incisional pin and a second pin placed through a 5-mm stab incision to reduce skin tension, particularly in flexion. Bone registration and intraoperative planning are performed using the MAKO robotic system within a functional alignment philosophy. All bone cuts are performed using a mobile window technique at 90° flexion, except for the anterior chamfer cut, which is carried out at 120° flexion to allow adequate clearance between the saw blade and the tibial array. This technique was performed in 82 patients, through which we achieved encouraging early wound healing and high patient satisfaction.
Conclusion:
The MISI technique offers a reproducible, incision-sparing approach to minimize soft-tissue trauma in robotic TKA. Prospective studies evaluating complication rates, recovery, and patient-reported outcomes are warranted to validate its clinical benefits and determine optimal patient selection.
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