From Digital Planning to Surgical Precision: Assessing the Accuracy of NemoFAB in Orthognathic Surgery

Background/Objectives: Three-dimensional virtual surgical planning (VSP) is increasingly central to contemporary orthognathic surgery, enhancing diagnostic precision and enabling more reliable forecasts of postoperative outcomes. NemoFAB (Nemotec, Madrid, Spain) is a recently developed digital platform that integrates CBCT data, digital dental models, and facial photographs into a single workflow. Despite its growing clinical use, independent validation of its predictive accuracy remains limited. This study evaluated how closely NemoFAB virtual predictions corresponded to actual postoperative results using standardized cephalometric parameters. Methods: Forty adult patients with dento-maxillofacial deformities requiring combined orthodontic–surgical treatment were included. Eleven cephalometric variables—common to both WebCeph (2D) and NemoFAB (3D)—were measured preoperatively, virtually in NemoFAB, and postoperatively. AI-assisted landmark placement was manually verified by two orthodontists. Statistical analyses included repeated-measures ANOVA, paired t-tests, Bland–Altman plots, and intraclass correlation coefficients (ICC) to evaluate agreement and predictive accuracy. Results: Overjet, overbite, maxillary incisor inclination, maxillary incisor exposure, mandibular incisor projection to the True Vertical Line, and occlusal plane angulation all showed statistically significant changes after surgery (p < 0.05). Bland–Altman analysis demonstrated the narrowest limits of agreement in Nemo–Post comparisons, indicating strong predictive alignment. ICC values showed excellent agreement for incisor angulation (ICC = 0.921–0.984) and Pogonion projection (ICC = 0.943). Consistently poor pre-Nemo agreement reflected expected discrepancies between initial anatomy and planned surgical correction. Conclusions: When predicting skeletal and dentoalveolar changes brought about by orthognathic surgery, NemoFAB showed a high degree of agreement, especially for parameters that are directly impacted by jaw repositioning. Its strong concordance with postoperative outcomes supports its reliability for virtual planning, interdisciplinary coordination, and surgical execution. Advances in soft-tissue modeling and AI-driven automation may further enhance predictive accuracy.