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基于深度学习的心脏成像数据测量及其在心脏性猝死诊断中的应用

[Deep Learning-Based Cardiac Imaging Data Measurement and Its Application in Diagnosis of Sudden Cardiac Death].

作者信息

Li Z H, Liu N G, Dong H W, Li L J, He H H, Lin L H, Liu Q, Yang M Z

机构信息

Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.

出版信息

Fa Yi Xue Za Zhi. 2021 Aug;37(4):546-554. doi: 10.12116/j.issn.1004-5619.2021.410503.

Abstract

In the field of forensic medicine, diagnosis of sudden cardiac death is limited by subjective factors and manual measurement methods, so some parameters may have estimation deviation or measurement deviation. As postmortem CT imaging plays a more and more important role in the appraisal of cause of death and cardiopathology research, the application of deep learning such as artificial intelligence technology to analyze vast amounts of cardiac imaging data has provided a possibility for forensic identification and scientific research workers to conduct precise diagnosis and quantitative analysis of cardiac diseases. This article summarizes the main researches on deep learning in the field of cardiac imaging in recent years, and proposes a feasible development direction for the application of deep learning in the virtual anatomy of sudden cardiac death at present.

摘要

在法医学领域,心源性猝死的诊断受主观因素和手工测量方法的限制,因此一些参数可能存在估计偏差或测量偏差。随着尸体CT成像在死因鉴定和心脏病理学研究中发挥越来越重要的作用,应用人工智能技术等深度学习方法分析大量心脏影像数据,为法医鉴定和科研工作者对心脏疾病进行精确诊断和定量分析提供了可能。本文总结了近年来心脏成像领域深度学习的主要研究成果,并针对目前深度学习在心源性猝死虚拟解剖中的应用提出了可行的发展方向。

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