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
{"title":"机器学习的一般动作评估:为什么这么难?","authors":"W. Schmidt, M. Regan, M. Fahey, A. Paplinski","doi":"10.21037/JMAI.2019.06.02","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.06.02","citationCount":"9","resultStr":"{\"title\":\"General movement assessment by machine learning: why is it so difficult?\",\"authors\":\"W. Schmidt, M. Regan, M. Fahey, A. Paplinski\",\"doi\":\"10.21037/JMAI.2019.06.02\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":73815,\"journal\":{\"name\":\"Journal of medical artificial intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.21037/JMAI.2019.06.02\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medical artificial intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21037/JMAI.2019.06.02\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medical artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21037/JMAI.2019.06.02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.30
自引率
0.00%
发文量
0
期刊最新文献
Artificial intelligence in periodontology and implantology—a narrative review Exploring the capabilities and limitations of large language models in nuclear medicine knowledge with primary focus on GPT-3.5, GPT-4 and Google Bard Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records Analysis of factors influencing maternal mortality and newborn health—a machine learning approach Efficient glioma grade prediction using learned features extracted from convolutional neural networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1