Three-dimensional evaluation of the association between tongue position and upper airway morphology in adults: A cross-sectional study.

Objective: This study aimed to evaluate the association between low tongue position (LTP) and the volume and dimensions of the nasopharyngeal, retropalatal, retroglossal, and hypopharyngeal segments of the upper airway. Methods: A total of 194 subjects, including 91 males and 103 females were divided into a resting tongue position (RTP) group and a LTP group according to their tongue position. Subjects in the LTP group were divided into four subgroups (Q1, Q2, Q3, and Q4) according to the intraoral space volume. The 3D slicer software was used to measure the volume and minimum and average cross-sectional areas of each group. Airway differences between the RTP and LTP groups were analyzed to explore the association between tongue position and the upper airway. Results: No significant differences were found in the airway dimensions between the RTP and LTP groups. For both retropalatal and retroglossal segments, the volume and average cross-sectional area were significantly greater in the patients with extremely low tongue position. Regression analysis showed that the retroglossal airway dimensions were positively correlated with the intraoral space volume and negatively correlated with A point-nasion-B point and palatal plane to mandibular plane. Males generally had larger retroglossal and hypopharyngeal airways than females. Conclusions: Tongue position did not significantly influence upper airway volume or dimensions, except in the extremely LTP subgroup.

Neural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes.

Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models (k-prototype and semi-supervised clustering, SSC) and a conventional risk score (Stroke Prognostic Instrument-II, SPI-II) to obtain a distinct distribution of 1-year vascular events. Ultimately, 7,650 patients were included, and the 1-year primary outcome event occurred in 13.1%. The DLC-Kuiper UB model had a significantly higher C-index (0.674), log-rank score (153.1), and Brier score (0.08) than the other cluster models (SSC and DLC-MMD) and the SPI-II score. There were significant differences in primary outcome events among the 3 clusters (41.7%, 13.4%, and 6.5% in clusters 0, 1, and 2, respectively) when the DLC-Kuiper UB model was used. A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic stroke.