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

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.

Motor subtypes and other risk factors associated with drooling in Parkinson's disease patients.

OBJECTIVE: To explore the relationship between motor subtypes and drooling, and other risk factors associated with drooling in a large cohort of Chinese patients with Parkinson's disease (PD). METHODS: A total of 586 PD patients were enrolled in this study. Unified Parkinson's disease rating scale (UPDRS) and Hoehn & Yahr stage (H & Y stage) scale, Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Hamilton Rating Scale for Depression-24 item (HRSD), and Mini-Mental State Examination (MMSE) were applied to all subjects. Salivary subscores of UPDRS part II were used to evaluate drooling. Constipation was diagnosed using the Rome III criteria. RESULTS: The prevalence of drooling in this cohort is 54.6% (320/586). Non-TD subtype PD patients tend to have higher daily levodopa-equivalent dose (LED), H & Y stage, UPDRS I, UPDRS II, and UPDRS III scores, HRSD score and ESS score, a higher percentage of levodopa treatment, drooling, dyskinesia, and constipation. After adjusting for confounders, non-TD subtype, male sex, UPDRS III score, ESS and PSQI scores, and constipation were still associated with drooling, with corresponding Odds ratios and 95% confidence intervals (95% CIs) were 1.865 (95% CI, 1.137-3.060), 1. 951 (95% CI, 1.326-2.869), 1.024 (95% CI, 1.002-1.046), 1.064 (95% CI, 1.024-1.105), 1.058 (95% CI, 1.000-1.119), and 1.603 (95% CI, 1.092-2.353), respectively. CONCLUSION: Drooling is common, even in mild-to-moderate PD patients. PD patients with non-TD subtype are at a higher risk of drooling. Male sex, motor severity, excessive daytime sleepiness, poor nighttime sleep, and constipation are also associated with drooling in patients with PD.