Virtual reconstruction and clinical verification of maxillary defect based on deep learning

Abstract

Objective: To construct a virtual reconstruction method including midspan maxillary defects and provide clinical reference by training a generative adversarial network (GAN) model. Methods: The CT data of middle-aged Han patients with oral diseases who visited the Department of Radiology, West China Hospital of Stomatology, Sichuan University from June 2015 to June 2022 were collected, where the CT data of 100 healthy maxilla and 15 maxillary defects (5 simple unilateral defects, 5 unilateral defects involving zygomatic bone, 5 midspan defects) were selected. Mimics was used to create spherical phantom and simulate bone defects around the healthy maxillas, including simple unilateral defects, unilateral defects involving zygomatic bone and midspan defects. The original image was set as the correct reference for the reconstruction: artificial defects paired with the correct reference were divided into training set (n=70), validation set (n=20) and test set (n=10), where the first two were used to train the GAN model, and the test set was used to evaluate the GAN performance. Data from 15 clinical defects were imported into the trained GAN model for reconstruction, with mirroring and GAN-based virtual reconstruction for unilateral clinical defects, and only the latter method was adopted for midspan defects. The reconstruction results were divided into mirror reconstruction group (n=10), unilateral defect GAN reconstruction group (n=10) and midspan defect GAN reconstruction group (n=5). The test set, mirror reconstruction group, and unilateral defect GAN reconstruction group were quantitatively evaluated, whose quantitative indicators were Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95), and the group results were subjected to one-way ANOVA and Tukey test. The test set, mirror reconstruction group, unilateral defect GAN reconstruction group and midspan defect GAN reconstruction group were qualitatively scored, and Kruskal-Wallis test and Bonferroni correction were used for the total score of each group. Results: The total differences in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group DCS (0.891±0.049, 0.721±0.047, 0.778±0.057, respectively) and HD95 [(3.58±1.51), (5.19±1.38), (4.51±1.10) mm, respectively] were statistically significant (F=28.08, P<0.001; F=3.62, P=0.041); among them, the test set DSC was significantly larger than the mirror reconstruction group (P<0.05), and the test set HD95 was significantly less than the mirror reconstruction group (P<0.05). Overall difference in qualitative total scores [8 (1), 6 (2), 6 (2), and 4 (2) points, respectively] in the test set, mirror reconstruction group, unilateral defect GAN reconstruction group, and midspan defect GAN reconstruction group were statistical significance (H=18.13, P<0.001); pairwise comparison showed that the total score of the test set was significantly higher than that of the mirror reconstruction group (P<0.05). Conclusions: The virtual reconstruction method based on GAN proposed in this study has better virtual reconstruction effect of unilateral defect than mirror technique, and can also realize virtual reconstruction of maxillary midspan defect.