Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature.

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Radiology Reports Pub Date : 2023-01-01 Epub Date: 2022-12-12 DOI:10.1007/s40134-022-00407-8

Pouria Rouzrokh, Bardia Khosravi, Sanaz Vahdati, Mana Moassefi, Shahriar Faghani, Elham Mahmoudi, Hamid Chalian, Bradley J Erickson

{"title":"Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature.","authors":"Pouria Rouzrokh, Bardia Khosravi, Sanaz Vahdati, Mana Moassefi, Shahriar Faghani, Elham Mahmoudi, Hamid Chalian, Bradley J Erickson","doi":"10.1007/s40134-022-00407-8","DOIUrl":null,"url":null,"abstract":"Purpose of review: In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI).Recent findings: During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research.Summary: ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML.Supplementary information: The online version contains supplementary material available at 10.1007/s40134-022-00407-8.","PeriodicalId":37269,"journal":{"name":"Current Radiology Reports","volume":"11 2","pages":"34-45"},"PeriodicalIF":1.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742664/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Radiology Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40134-022-00407-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/12/12 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose of review: In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI).

Recent findings: During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research.

Summary: ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML.

Supplementary information: The online version contains supplementary material available at 10.1007/s40134-022-00407-8.

Abstract Image

查看原文本刊更多论文

心血管成像中的机器学习:已发表文献的范围综述。

综述目的:在本研究中，我们计划并进行了文献综述，以了解机器学习(ML)在心血管成像(CVI)中的研究情况。最近的发现:在我们的搜索过程中，我们发现许多研究开发或利用现有的ML模型进行分割、分类、目标检测、生成和回归应用，涉及心血管成像数据。我们首先定量调查了纳入我们综述的所有研究的研究特征、数据处理、模型开发和绩效评估的不同方面。然后，我们用定性综合来补充这些发现，以突出研究文献中的共同主题，并提供建议，为即将进行的研究铺平道路。摘要:机器学习是人工智能(AI)的一个子领域，它使计算机能够从数据中学习类似人类的决策。由于其新颖的应用，机器学习越来越受到医疗保健行业研究人员的关注。心血管成像是医学成像研究的一个活跃领域，有很多新技术的应用空间，如ml。补充信息:在线版本包含补充材料，可在10.1007/s40134-022-00407-8。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Current Radiology Reports Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

1.60

自引率

14.30%

发文量

期刊介绍： Current Radiology Reports aims to offer expert review articles on the most significant recent developments in the field of radiology. By providing clear, insightful, balanced contributions, the journal intends to serve all those who use imaging technologies and related techniques to diagnose and treat disease. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas across the field. Section Editors select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An Editorial Board of more than 20 internationally diverse members reviews the annual table of contents, ensures that topics include emerging research, and suggests topics of special importance to their country/region. Topics covered may include abdominal imaging (including virtual colonoscopy); cardiac imaging; clinical MRI; dual-source CT; interventional radiology; minimal invasive procedures and high-frequency focused ultrasound; musculoskeletal imaging; neuroimaging; nuclear medicine; pediatric imaging; PET, PET-CT, and PET-MRI; radiation exposure and reduction; translational molecular imaging; and ultrasound.