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基于计算机的视频分析来识别烦躁运动,并通过该分析来预测脑瘫,当基于两个视频记录时,其结果更加准确。

Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

机构信息

Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway.

出版信息

Physiother Theory Pract. 2013 Aug;29(6):469-75. doi: 10.3109/09593985.2012.757404. Epub 2013 Jan 23.

Abstract

This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies.

摘要

本研究评估了在评估时的过期胎龄以及使用一个或两个视频记录来检测多动运动(FM)和使用计算机视觉软件预测脑瘫(CP)的作用。使用了 52 名早产儿和足月儿(24 名男孩,28 名女孩;26 名早产儿)在过期胎龄 9 至 17 周之间的记录。使用计算机视觉软件对记录进行了分析。从后续视频帧之间的差异中得出运动变量,用于定量分析。估计了第一个和第二个记录的敏感性、特异性和曲线下面积,或两者的平均值。FM 根据总体运动评估的 Prechtl 方法进行分类。CP 状态在 2 岁时报告。9 名儿童患有 CP,所有记录均未出现 FM。两次记录的运动质心(CSD)的均值变异性比仅使用一次记录更准确,可识别出所有在 2 岁时被诊断为 CP 的儿童。评估时的年龄并未影响 FM 的检测或 CP 的预测。未来的研究应确认基于两个记录的计算机视觉技术在识别 FM 和预测 CP 方面的准确性。

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