深度学习用于超声心动图中的自动钙检测。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-08-28 DOI:10.1186/s13040-024-00381-1
Luís B Elvas, Sara Gomes, João C Ferreira, Luís Brás Rosário, Tomás Brandão
{"title":"深度学习用于超声心动图中的自动钙检测。","authors":"Luís B Elvas, Sara Gomes, João C Ferreira, Luís Brás Rosário, Tomás Brandão","doi":"10.1186/s13040-024-00381-1","DOIUrl":null,"url":null,"abstract":"<p><p>Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve - achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve - which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11351547/pdf/","citationCount":"0","resultStr":"{\"title\":\"Deep learning for automatic calcium detection in echocardiography.\",\"authors\":\"Luís B Elvas, Sara Gomes, João C Ferreira, Luís Brás Rosário, Tomás Brandão\",\"doi\":\"10.1186/s13040-024-00381-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve - achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve - which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.</p>\",\"PeriodicalId\":48947,\"journal\":{\"name\":\"Biodata Mining\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11351547/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biodata Mining\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13040-024-00381-1\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biodata Mining","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13040-024-00381-1","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

心血管疾病是世界上最主要的死亡原因,而心血管成像技术是无创诊断的主要手段。主动脉瓣狭窄是一种致命的心脏疾病,主动脉瓣钙化会持续数年。利用深度学习(DL)算法开发的数据驱动工具可以对医学影像数据进行处理和分类,提供可靠的快速诊断,从而提高医疗保健的效率。一项关于将深度学习应用于病理钙检测的医学图像的系统性综述得出结论,该领域已有成熟的技术,主要使用 CT 扫描,但以辐射暴露为代价。超声心动图是一种尚未开发的检测钙的替代方法,但仍需要技术发展。本文开发了一种基于卷积神经网络(CNN)的全自动方法来检测超声心动图图像中的主动脉钙化,该方法由两个基本过程组成:(1)定位主动脉瓣的物体检测器--精确度达到 95%,召回率达到 100%;(2)识别瓣膜中钙结构的分类器--精确度达到 92%,召回率达到 100%。这项工作的成果是实现了主动脉瓣钙化这一致命流行病的超声心动图自动化检测。
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
Deep learning for automatic calcium detection in echocardiography.

Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve - achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve - which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.

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
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
期刊最新文献
Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion. Modeling heterogeneity of Sudanese hospital stay in neonatal and maternal unit: non-parametric random effect models with Gamma distribution. Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability. Priority-Elastic net for binary disease outcome prediction based on multi-omics data. A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies.
×
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