3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

Abstract

Three-dimensional technologies have had a wide diffusion in several fields of application throughout the last decades; medicine is no exception and the interest in their introduction in clinical applications has grown with the refinement of such technologies. We focus on the application of 3D methodologies in maxillofacial surgery, where they can give concrete support in surgical planning and in the prediction of involuntary facial soft-tissue changes after planned bony repositioning. The purpose of this literature review is to offer a panorama of the existing prediction methods and software with a comparison of their reliability and to propose a series of still pending issues. Various software are available for surgical planning and for the prediction of tissue displacements, but their reliability is still an unknown variable in respect of the accuracy needed by surgeons. Maxilim, Dolphin and other common planning software provide a realistic result, but with some inaccuracies in specific areas of the face; it also is not totally clear how the prediction is obtained by the software and what is the theoretical model they are based on.

Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals

“…Patients presenting dentofacial deformities are commonly subject to combine orthodontic and surgical treatment[ 12 ]. A maxillofacial skeletal analysis is an important part of diagnosis and treatment planning [ 13 ], and it is used to assess the vertical, lateral, and anteroposterior positions of the jaws using posteroanterior (PA) and lateral (Lat) cephalogram.…”

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

“…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 ].…”

Intraoperative surgery room management: A deep learning perspective

“…In the last decades, several medical procedures have improved year by year with the help of technology 1,2 ; among these, great progress has been achieved with deep learning (DL) 3 paradigms. DL is becoming more and more widely used in the world of computer vision technologies, giving astonishing results in different fields of application, such as text classification 4 and face recognition 5 .…”