Assessment of the Accuracy of Bilateral Sagittal Split Horizontal Osteotomy Using Ascending Ramus Geometry: A Human...

Sagittal split osteotomy (SSO) is a routine procedure, but postoperative nerve disturbance (PNSD) remains a challenge. This study aimed to perform medial horizontal osteotomy (MHO) guided by ascending ramus geometry and avoid excessive deep medial dissection to locate the lingula. The authors hypothesized that the MHO would be located midway between the 2 main reference points: the subcoronoid (the point of concurrency of an internal and external oblique ridge of the ascending ramus of the mandible) and the medial point at the wideband at the retromolar trigon. SSO was performed on 40 cadaveric hemimandibles. In each cadaver head, the proposed hypothesis was applied to the left side (experimental side), and conventional MHO was performed which required medial dissection and location of the lingula before performing the osteotomy on the right side of the mandible. Variables such as mandibular foramen (MF) position, lingual split scale (LSS), and inferior border fractures were analyzed. The independent variables were the mandibular canal, mylohyoid groove, and dental status/alveolar ridge classification. Wilcoxon signed-rank test for categorical data. Spearman rank correlation coefficient was used to correlate different study variables. There was no statistically significant difference between the 2 sides but tooth status strongly correlated with MHO position. Consequently, the MHO was located above the lingual region in all dentate cadavers. The location of the nerve was reported at distal segments of the experimental and control sides in 45% and 40%, respectively, at P=0.07. There was a strong correlation between the location of the IAN and the type of LSS and the presence of nerve mylohyoid groove at -584 and 0.645 (P=0.007 and 0.002, respectively) for both sides. Using ascending ramus geometry avoids excessive dissection, reducing PNSD risk, would contribute in developing or ameliorating the current machine learning algorithms to effectively perform surgical planning of dentomaxillofacial deformities using artificial intelligence (AI).