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Developmental changes of upper airway dimensions in children.

儿童上气道尺寸的发育变化。

  • 影响因子:1.74
  • DOI:10.1111/pan.13832
  • 作者列表:"Luscan R","Leboulanger N","Fayoux P","Kerner G","Belhous K","Couloigner V","Garabedian EN","Simon F","Denoyelle F","Thierry B
  • 发表时间:2020-01-29
Abstract

BACKGROUND:Knowledge about airway dimensions during child growth is of paramount importance for pediatric clinical practice. Decisions about airway management in children are based on relatively limited, imprecise or incomplete data about airway size. AIMS:The aim of this work was to determine the anatomical development and size of airway structures from birth to adolescence using high resolution computed tomography scans (HRCTScans) and to study the correlation between airway measurements and biometric data. METHODS:We conducted a retrospective study of all HRCTscans including the respiratory tract, performed in our tertiary pediatric center (for reasons unrelated to airway symptoms) between June 1st , 2016 and October 15th , 2017, on children aged from 1 day to 14 years old. On each scan, 23 measurements of the larynx, trachea and mainstem bronchi were performed. Patients were stratified into 16 groups according to their age. We calculated median value for each measurement in each group. Statistical models were calculated to explore correlation between measurements and age or weight. RESULTS:A total of 192 CT-scans were included (127 boys / 65 girls). The mean age was 7 years. The correlations between airway measurements and age or weight were always significant. The relationship between measurements and age was found to be suitably represented by a cubic polynomial equation suggesting that the airway has a rapid growth phase in the first 3 years, followed by a slow growth phase and a second rapid growth phase during adolescence. The most relevant biometric parameter was age concerning 21 of the measurements. CONCLUSIONS:This comprehensive anatomical database of upper airway dimensions provide important data in the field of pediatric airway anatomy, particularly relating to the cricoid. We demonstrated that laryngeal, tracheal and bronchial parameters correlate better to age and have three different growth phases.

摘要

背景: 儿童成长过程中关于气道尺寸的知识对于儿科临床实践至关重要。关于儿童气道管理的决定是基于关于气道大小的相对有限、不精确或不完整的数据。 目的: 这项工作的目的是使用高分辨率计算机断层扫描 (hrctscan) 确定从出生到青春期气道结构的解剖发育和大小并研究气道测量值与生物特征数据之间的相关性。 方法: 我们对 2016年6月1日至 2017年10月15日期间在我们三级儿科中心进行的所有 hrctscan (与气道症状无关的原因) 进行了回顾性研究,包括呼吸道。 1 天至 14 岁的儿童。每次扫描时,对喉、气管和主支气管进行 23 次测量。根据年龄将患者分为 16 组。我们计算了每组中每次测量的中值。计算统计模型,探讨测量值与年龄或体重之间的相关性。 结果: 共纳入 192 例 ct扫描 (127 例男孩/65 例女孩)。平均年龄 7 岁。气道测量值与年龄或体重之间的相关性总是显著的。发现测量值和年龄之间的关系由三次多项式方程适当表示,表明气道在头 3 年具有快速生长期, 随后是青春期的缓慢生长期和第二个快速生长期。最相关的生物特征参数是关于 21 次测量的年龄。 结论: 这个全面的上气道解剖数据库提供了小儿气道解剖领域的重要数据,特别是与环状软骨相关的数据。我们证明喉、气管和支气管参数与年龄相关性更好,有三个不同的生长阶段。

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DOI:10.1007/s00464-019-07334-4
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影响因子:3.31
发表时间:2020-01-02
DOI:10.1007/s10916-019-1481-4
作者列表:["Matava, Clyde","Pankiv, Evelina","Raisbeck, Sam","Caldeira, Monica","Alam, Fahad"]

METHODS:Background The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings. Methods Following institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared. Results The overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed. Conclusions We report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.

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影响因子:3.84
发表时间:2020-01-01
来源期刊:Chest
DOI:10.1016/j.chest.2019.06.018
作者列表:["Dhooria S","Chaudhary S","Ram B","Sehgal IS","Muthu V","Prasad KT","Aggarwal AN","Agarwal R"]

METHODS:BACKGROUND:The optimal mode of delivering topical anesthesia during flexible bronchoscopy remains unknown. This article compares the efficacy and safety of nebulized lignocaine, lignocaine oropharyngeal spray, or their combination. METHODS:Consecutive subjects were randomized 1:1:1 to receive nebulized lignocaine (2.5 mL of 4% solution, group A), oropharyngeal spray (10 actuations of 10% lignocaine, group B), or nebulization (2.5 mL, 4% lignocaine) and two actuations of 10% lignocaine spray (group C). The primary outcome was the subject-rated severity of cough according to a visual analog scale. The secondary outcomes included bronchoscopist-rated severity of cough and overall procedural satisfaction on a visual analog scale, total lignocaine dose, subject's willingness to undergo a repeat procedure, adverse reactions to lignocaine, and others. RESULTS:A total of 1,050 subjects (median age, 51 years; 64.8% men) were included. The median (interquartile range) score for subject-rated cough severity was significantly lower in group B compared to group C or group A (4 [1-10] vs 11 [4-24] vs 13 [5-30], respectively; P < .001). The bronchoscopist-rated severity of cough was also the least (P < .001), and the overall satisfaction was highest in group B (P < .001). The cumulative lignocaine dose administered was the least in group B (P < .001). A significantly higher proportion of subjects (P < .001) were willing to undergo a repeat bronchoscopy in group B (73.7%) than in groups A (49.1%) and C (59.4%). No lignocaine-related adverse events were observed. CONCLUSIONS:Ten actuations of 10% lignocaine oropharyngeal spray were superior to nebulized lignocaine or their combination for topical anesthesia during diagnostic flexible bronchoscopy. TRIAL REGISTRY:ClinicalTrials.gov; No.: NCT03109392; URL: www.clinicaltrials.gov.

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