Abstract
Patients with severe facial deformities present serious dysfunctionalities along with an unsatisfactory aesthetic facial appearance. Several methods have been proposed to specifically plan the interventions on the patient’s needs, but none of these seem to achieve a sufficient level of accuracy in predicting the resulting facial appearance. In this context, a deep knowledge of what occurs in the face after bony movements in specific surgeries would give the possibility to develop more reliable systems. This study aims to propose a novel 3D approach for the evaluation of soft tissue zygomatic modifications after zygomatic osteotomy; geometrical descriptors usually involved in face analysis tasks, i.e., face recognition and facial expression recognition, are here applied to soft tissue malar region to detect changes in surface shape. As ground truth for zygomatic changes, a zygomatic openness angular measure is adopted. The results show a high sensibility of geometrical descriptors in detecting shape modification of the facial surface, outperforming the results obtained from the angular evaluation.
Highlights
The last two decades have seen the introduction to medicine of new methodologies founded on 3D technologies enabling a new approach in all fields, from diagnostic to surgery
Concerning the surgical field, maxillofacial and orthognathic surgery has benefitted from the advance in computed tomography technology that enabled systems to support planning and simulation of customized operations [1,2]
For all patients who underwent the malar augmentation, arctanF highlighted a change in the facial shape; in all cases more than 55% of the points showed values of arctanF that have risen from pre- to post-operative
Summary
The last two decades have seen the introduction to medicine of new methodologies founded on 3D technologies enabling a new approach in all fields, from diagnostic to surgery. The accuracy introduced with 3D CBCT allowed the development of software intended to predict future facial appearance of patients after simulated bony movements [3,4,5,6,7]. Often, these predictions have been judged to be somewhat inaccurate in forecasting soft tissue displacements [8,9,10,11,12]. To better understand how a more reliable prediction tool could be implemented, it is of primary importance to study and quantify the changes occurring in patients who already underwent the surgeries whose effects are to be previewed [13,14]
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