机器学习的一般动作评估:为什么这么难?

W. Schmidt, M. Regan, M. Fahey, A. Paplinski
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引用次数: 9

摘要

目前,澳大利亚每活产脑瘫(CP)的发病率在0.14%至0.2%之间,全球范围内的发病率60年来一直保持在0.2%。通常,脑瘫的诊断会推迟到2岁左右;这种延迟降低了长期阳性患者结果的可能性。目前的早期检测是通过在妊娠后10至20周对新生儿进行视觉检查。一个基于拍摄婴儿并通过人工智能处理视频的筛查计划将增加早期检测和干预。本文概述了一种用于新生儿视频分类的递归深度神经网络解决方案的实际发展和初步结果,该解决方案专门针对CP,使用了澳大利亚最大的坐立不安运动数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
General movement assessment by machine learning: why is it so difficult?
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.
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CiteScore
2.30
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