Quantitative validation of a computer-aided maxillofacial planning system, focusing on soft tissue deformations

Abstract

Aim: The aim of this study was to evaluate the accuracy of 3D soft tissue predictions generated by a computer-aided maxillofacial planning system in patients undergoing orthognathic surgery.

Methods and materials: Twenty patients with dentofacial dysmorphosis were treated with orthognathic surgery after a preoperative orthodontic treatment. Fourteen patients had an Angle Class II malocclusion; three patients had an Angle class III malocclusion, and three patients had an Angle Class I malocclusion. Skeletal asymmetry was observed in six patient. The surgeries were planned using the Maxilim software. Computer assisted surgical planning was transferred to the patient by digitally generated splints. The validation procedures were performed in the following steps: (1) Standardized registration of the pre- and postoperative Cone Beam CT volumes; (2) Automated adjustment of the bone-related planning to the actual operative bony displacement; (3) Simulation of soft tissue changes; (4) Calculation of the soft tissue differences between the predicted and the postoperative results by distance mapping.

Statistical analysis and results: Eighty four percent of the mapped distances between the predicted and actual postoperative results measured between -2 mm and +2 mm. The mean absolute linear measurements between the predicted and actual postoperative surface was 1.18. Our study shows the overall prediction was dependent on neither the surgical procedures nor the dentofacial deformity type.

Conclusion: Despite some shortcomings in the prediction of the final position of the lower lip and cheek area, this software promises a clinically acceptable soft tissue prediction for orthognathic surgical procedures.

Keywords: 3D planning; orthognathic surgery; soft tissue prediction; virtual surgery.

Figure 1

Patient positioned in natural head position (NHP). The NHP was respected during the pre- and post-operative scanning procedure

Figure 2

Three-dimensional reconstruction of the pre- (white) and post- (green) operative skull before (a) and after registration (b). The yellow frame indicates the selected registration area

Figure 3

The rigid Iterative Closest Point algorithm was used to align the surfaces of osteotomised maxilla (brown) and the proximal segment of the mandible (blue) with the postoperative bony segments (green). (a) Before alignment and (b) after alignment. The white area shows the unaltered bony structures. The yellow colour shows the osteotomised chin

Figure 4

(a) Simulated soft tissue, (b) actual postoperative profile, and (c) overlap simulated-actual postoperative profile

Figure 5

Distance map with the measured errors projected onto the postoperative facial skin surface. Green indicates under-simulated regions (postoperative in front of prediction)

Figure 6

The boxplot presents the mean signed distance (red bar), the 25-75% distance range (blue box) and the 5-95% distance range (dotted lines) for each data set

Figure 7

Color-coded distance maps for all patients. Green is −2 mm and red is +2 mm. The performed procedure is mentioned below each distance map. LF I: Le Fort I; BSSO: Bilateral sagittal split osteotomy; int.: Intrusion; ext.: Extrusion; adv.: Advancement; rot.: Rotation; L.: Left; R.: Right; VME: Vertical maxillary excess; Bimax.: Bimaxillary; Hypo.: Hypoplasia; Vert.: Vertical; Horz.: Horizontal; Asym.: Asymmetric

Figure 8

The overlapped simulated (brown) and actualpostoperative (green) profiles