基于移动设备的婴儿头部滞后视频筛查：一项探索性研究。

Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study.

作者信息

Chung Hao-Wei, Chang Che-Kuei, Huang Tzu-Hsiu, Chen Li-Chiou, Chen Hsiu-Lin, Yang Shu-Ting, Chen Chien-Chih, Wang Kuochen

机构信息

Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

Department of Biological Science and Technology, National Yang Ming Chiao-Tung University, Hsinchu 300, Taiwan.

出版信息

Children (Basel). 2023 Jul 18;10(7):1239. doi: 10.3390/children10071239.

DOI:10.3390/children10071239

PMID:37508736

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10378382/

Abstract

INTRODUCTION

Video-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants' motor skills.

METHODS

A total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge.

RESULTS

The average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively.

CONCLUSIONS

An AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.

摘要

引言

基于视频的自动运动分析已被用于识别婴儿运动发育迟缓。为克服实验室记录图像和训练数据集的局限性，本研究旨在使用手机拍摄的视频开发一种人工智能（AI）模型，以评估婴儿的运动技能。

方法

家长使用移动设备拍摄了41名高危婴儿的总共270个视频。基于哈默史密斯运动评估的拉起坐起（PTS）水平，我们设定了运动技能评估。这些视频包括84个0级、106个1级和80个3级记录。我们使用基于模糊方法的全身姿态估计和三维变换来开发一个AI模型。该模型用两种类型的向量进行训练：全身骨骼和具有领域知识的关键点。