ArticleJournal of orthopaedic surgery and research2026
A minimal-incision and minimal-soft-tissue-injury (MISI) technique for robot-assisted total knee arthroplasty: a preliminary report.
Article in Journal of orthopaedic surgery and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundRobot-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.
methodsThe 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.
conclusionThe 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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