Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression

Abstract

Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient’s biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103 mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.

A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning

“…The GAN technique, a new type of deep learning technique, has been gaining more attention in the medical field. In previous studies, there has been a prediction for a soft tissue deformity after an osteotomy, 29,30 a probabilistic finite element model for the prediction of postoperative facial images for orthognathic surgery, 31 and a similar technique for orbital plastic surgery. 32 These studies require the acquisition of whole facial data collected through computed tomography or magnetic resonance imaging.…”

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

“…Inherently, the accuracy of the soft tissue prediction model will depend on the training data, which might lead to inaccuracies when interpolating these results onto other cohorts, for example, paediatric patients. Other statistical approaches relying on patient data have been described in the literature [ 43 – 45 ].…”

Design, development and clinical validation of computer-aided surgical simulation system for streamlined orthognathic surgical planning

“…Several models have been used to simulate soft-tissue deformations both commercially and in research, including: empirical-based models [8,9,46,47], mass spring models [48–50], mass tensor models [51,52] and finite element models (FEM) [47,49,51–56]. Among these, FEM is reported to be the most common, accurate and biomechanically relevant method [57–60]. However, preparation and computation time for FEM is significant (from hours to days).…”