Cranioplasty is a common surgical procedure for addressing skull defects following decompressive craniectomy. Although the procedure is relatively straightforward, the incidence of postoperative complications remains high, with severe complications potentially leading to the failure of the repair.
To assist clinicians in predicting the occurrence of postoperative complications after cranioplasty, we have developed a machine learning- based prediction model.
This model aims to enhance postoperative management, reduce the incidence of complications, and improve surgical outcomes.