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使用模式识别根据透视参数对高分辨率测压数据进行分类。

Classification of high-resolution manometry data according to videofluoroscopic parameters using pattern recognition.

机构信息

Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53792, USA.

出版信息

Otolaryngol Head Neck Surg. 2013 Jul;149(1):126-33. doi: 10.1177/0194599813489506. Epub 2013 Jun 1.

Abstract

OBJECTIVE

To determine if pattern recognition techniques applied to high-resolution manometry (HRM) spatiotemporal plots of the pharyngeal swallow can identify features of disordered swallowing reported on the Modified Barium Swallow Impairment Profile (MBSImP).

STUDY DESIGN

Case series evaluating new method of data analysis.

SETTING

University hospital.

SUBJECTS AND METHODS

Simultaneous HRM and videofluoroscopy was performed on 30 subjects (335 swallows) with dysphagia. Videofluoroscopic studies were scored according to the MBSImP guidelines while HRM plots were analyzed using a novel program. Pattern recognition using a multilayer perceptron artificial neural network (ANN) was performed to determine if 7 pharyngeal components of the MBSImP as well as penetration/aspiration status could be identified from the HRM plot alone. Receiver operating characteristic (ROC) analysis was also performed.

RESULTS

MBSImP parameters were identified correctly as normal or disordered at an average rate of approximately 91% (area under the ROC curve ranged from 0.902 to 0.981). Classifications incorporating two MBSImP parameters resulted in classification accuracies over 93% (area under the ROC curve ranged from 0.963 to 0.989).

CONCLUSION

Pattern recognition coupled with multiparameter quantitative analysis of HRM spatiotemporal plots can be used to identify swallowing abnormalities, which are currently assessed using videofluoroscopy. The ability to provide quantitative, functional data at the bedside while avoiding radiation exposure makes HRM an appealing tool to supplement and, at times, replace traditional videofluoroscopic studies.

摘要

目的

确定应用于高分辨率测压(HRM)时空图的模式识别技术是否可以识别在改良吞咽障碍评估量表(MBSImP)中报告的吞咽障碍特征。

研究设计

评估新数据分析方法的病例系列。

设置

大学医院。

受试者和方法

对 30 名吞咽困难患者(335 次吞咽)进行同步 HRM 和视频透视检查。根据 MBSImP 指南对视频透视研究进行评分,同时使用新程序对 HRM 图谱进行分析。使用多层感知器人工神经网络(ANN)进行模式识别,以确定是否可以仅从 HRM 图谱中识别 MBSImP 的 7 个咽部成分以及渗透/吸入状态。还进行了接收器操作特性(ROC)分析。

结果

MBSImP 参数以平均约 91%的准确率(ROC 曲线下面积范围从 0.902 到 0.981)被正确识别为正常或异常。纳入两个 MBSImP 参数的分类准确率超过 93%(ROC 曲线下面积范围从 0.963 到 0.989)。

结论

模式识别结合 HRM 时空图谱的多参数定量分析可用于识别吞咽异常,这些异常目前使用视频透视评估。在床边提供定量、功能性数据的能力,同时避免辐射暴露,使 HRM 成为一种有吸引力的工具,可以补充甚至有时替代传统的视频透视研究。

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