The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2024-11-12 DOI:10.1016/j.isci.2024.111374

Xueping Hu , Han Zhang , Federico Caobelli , Yan Huang , Yuchen Li , Jiajia Zhang , Kuangyu Shi , Fei Yu

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Abstract

The development of state-of-the-art algorithms for computer visualization has led to a growing interest in applying deep learning (DL) techniques to the field of medical imaging. DL-based algorithms have been extensively utilized in various aspects of cardiovascular imaging, and one notable area of focus is single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), which is regarded as the gold standard for non-invasive diagnosis of myocardial ischemia. However, due to the complex decision-making process of DL based on convolutional neural networks (CNNs), the explainability of DL results has become a significant area of research, particularly in the field of medical imaging. To better harness the potential of DL and to be well prepared for the ongoing DL revolution in nuclear imaging, this review aims to summarize the recent applications of DL in MPI, with a specific emphasis on the methods in explainable DL for the diagnosis and prognosis of MPI. Furthermore, the challenges and potential directions for future research are also discussed.

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深度学习在心肌灌注成像诊断和预后中的作用：系统综述

随着计算机可视化先进算法的发展，人们对将深度学习（DL）技术应用于医学成像领域的兴趣与日俱增。基于深度学习的算法已被广泛应用于心血管成像的各个方面，其中一个值得关注的领域是单光子发射计算机断层扫描（SPECT）心肌灌注成像（MPI），它被视为无创诊断心肌缺血的黄金标准。然而，由于基于卷积神经网络（CNN）的DL决策过程复杂，DL结果的可解释性已成为一个重要的研究领域，尤其是在医学成像领域。为了更好地利用可解释性逻辑的潜力，并为核成像领域正在进行的可解释性逻辑革命做好充分准备，本综述旨在总结可解释性逻辑在重大计划成像中的最新应用，特别强调可解释性逻辑中用于重大计划成像诊断和预后的方法。此外，还讨论了未来研究的挑战和潜在方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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