General movement assessment by machine learning: why is it so difficult?
General movement assessment by machine learning: why is it so difficult?
复制标题
机器学习的一般运动评估：为什么这么难？
DOI：
10.21037/jmai.2019.06.02
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Paplinski
中科院分区：
文献类型：
--
作者：
W. Schmidt;M. Regan;M. Fahey;A. Paplinski
目前，澳大利亚每例活产婴儿的脑瘫（CP）发病率在0.14%至0.2%之间，而全球范围内的这一发病率60年来一直保持在0.2%。通常，CP的诊断延迟到2岁左右;这种延迟降低了患者长期积极结局的可能性。目前的早期检测是通过对妊娠后10至20周的新生儿进行目视检查。基于拍摄婴儿并通过人工智能（AI）处理视频的筛查计划将允许增加早期检测和干预。本文概述了使用澳大利亚最大的烦躁运动数据集对新生儿视频进行分类的循环深度神经网络解决方案的实际开发和初步结果，特别是针对CP。
The current rate of cerebral palsy (CP) per live births in Australia is between 0.14% and 0.2%, worldwide the rate has been static for 60 years at 0.2%. Typically a CP diagnosis is delayed until around age 2 years; this delay decreases the likelihood of a long-term positive patient outcome. Current early detection is by visual examination of newborns 10 to 20 weeks post gestation. A screening program based on filming babies and processing the video via artificial intelligence (AI) will allow increased early detection and intervention. This paper outlines the practical development, and initial results from, a recurrent deep neural net solution for the classification of newborn videos, specifically targeting CP, using the largest fidgety movements dataset in Australia.