The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging
Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatr...
Gespeichert in:
Veröffentlicht in: | Pediatric radiology 2022-10, Vol.52 (11), p.2131-2138 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2138 |
---|---|
container_issue | 11 |
container_start_page | 2131 |
container_title | Pediatric radiology |
container_volume | 52 |
creator | Taylor, Andrew M. |
description | Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience. |
doi_str_mv | 10.1007/s00247-021-05218-1 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9537201</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2721999436</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-c2f1d6056f6f8ba2fabfa4ea0dd21f08f4ed8fc1bbdd1fdd2cbee49a22f21a763</originalsourceid><addsrcrecordid>eNp9UctuFDEQtBAR2Sz8AAc0EpdchrQfO48LEopIQIqUS3LiYPXY7YmjWXuxZyLx93izITwOnCx3laurXIy95fCBA7RnGUCotgbBa9gI3tX8BVtxJUXN-757yVYgoUBK9cfsJOd7AJAbLl-xY6l62QDvV-zbzR1VKU5URVdhmr3zxuNU-TDTNPmRgqFyqXZI1uOcvKkMJuvjA2azTJiqLY6B5jJPlGPAR36Z-TC-ZkcOp0xvns41u734fHP-pb66vvx6_umqNqpVc22E47aBTeMa1w0oHA4OFSFYK7iDzimynTN8GKzlrgzNQKR6FMIJjm0j1-zjQXe3DFuyhsKccNK7VHykHzqi138jwd_pMT7ofiNbAbwInD4JpPh9oTzrrc-m5MdAcclaNFy0slGFv2bv_6HexyWFEk-LVpR_75XcOxIHlkkx50Tu2QwHve9OH7rTpTv92J3eu3j3Z4znJ7_KKgR5IOQChZHS793_kf0J6oOoJQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2721999436</pqid></control><display><type>article</type><title>The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging</title><source>SpringerLink Journals - AutoHoldings</source><creator>Taylor, Andrew M.</creator><creatorcontrib>Taylor, Andrew M.</creatorcontrib><description>Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.</description><identifier>ISSN: 0301-0449</identifier><identifier>EISSN: 1432-1998</identifier><identifier>DOI: 10.1007/s00247-021-05218-1</identifier><identifier>PMID: 34936019</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial intelligence ; Artificial Intelligence in Pediatric Radiology ; Automation ; Cardiovascular disease ; Children & youth ; Heart ; Imaging ; Machine learning ; Magnetic resonance imaging ; Medicine ; Medicine & Public Health ; Neural networks ; Neuroradiology ; Nuclear Medicine ; Oncology ; Patients ; Pediatrics ; Radiology ; Scanners ; Ultrasound</subject><ispartof>Pediatric radiology, 2022-10, Vol.52 (11), p.2131-2138</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-c2f1d6056f6f8ba2fabfa4ea0dd21f08f4ed8fc1bbdd1fdd2cbee49a22f21a763</citedby><cites>FETCH-LOGICAL-c474t-c2f1d6056f6f8ba2fabfa4ea0dd21f08f4ed8fc1bbdd1fdd2cbee49a22f21a763</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00247-021-05218-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00247-021-05218-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34936019$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Taylor, Andrew M.</creatorcontrib><title>The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging</title><title>Pediatric radiology</title><addtitle>Pediatr Radiol</addtitle><addtitle>Pediatr Radiol</addtitle><description>Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial Intelligence in Pediatric Radiology</subject><subject>Automation</subject><subject>Cardiovascular disease</subject><subject>Children & youth</subject><subject>Heart</subject><subject>Imaging</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neural networks</subject><subject>Neuroradiology</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Radiology</subject><subject>Scanners</subject><subject>Ultrasound</subject><issn>0301-0449</issn><issn>1432-1998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9UctuFDEQtBAR2Sz8AAc0EpdchrQfO48LEopIQIqUS3LiYPXY7YmjWXuxZyLx93izITwOnCx3laurXIy95fCBA7RnGUCotgbBa9gI3tX8BVtxJUXN-757yVYgoUBK9cfsJOd7AJAbLl-xY6l62QDvV-zbzR1VKU5URVdhmr3zxuNU-TDTNPmRgqFyqXZI1uOcvKkMJuvjA2azTJiqLY6B5jJPlGPAR36Z-TC-ZkcOp0xvns41u734fHP-pb66vvx6_umqNqpVc22E47aBTeMa1w0oHA4OFSFYK7iDzimynTN8GKzlrgzNQKR6FMIJjm0j1-zjQXe3DFuyhsKccNK7VHykHzqi138jwd_pMT7ofiNbAbwInD4JpPh9oTzrrc-m5MdAcclaNFy0slGFv2bv_6HexyWFEk-LVpR_75XcOxIHlkkx50Tu2QwHve9OH7rTpTv92J3eu3j3Z4znJ7_KKgR5IOQChZHS793_kf0J6oOoJQ</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Taylor, Andrew M.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7RV</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20221001</creationdate><title>The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging</title><author>Taylor, Andrew M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-c2f1d6056f6f8ba2fabfa4ea0dd21f08f4ed8fc1bbdd1fdd2cbee49a22f21a763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial Intelligence in Pediatric Radiology</topic><topic>Automation</topic><topic>Cardiovascular disease</topic><topic>Children & youth</topic><topic>Heart</topic><topic>Imaging</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neural networks</topic><topic>Neuroradiology</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Patients</topic><topic>Pediatrics</topic><topic>Radiology</topic><topic>Scanners</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taylor, Andrew M.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Pediatric radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taylor, Andrew M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging</atitle><jtitle>Pediatric radiology</jtitle><stitle>Pediatr Radiol</stitle><addtitle>Pediatr Radiol</addtitle><date>2022-10-01</date><risdate>2022</risdate><volume>52</volume><issue>11</issue><spage>2131</spage><epage>2138</epage><pages>2131-2138</pages><issn>0301-0449</issn><eissn>1432-1998</eissn><abstract>Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34936019</pmid><doi>10.1007/s00247-021-05218-1</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0301-0449 |
ispartof | Pediatric radiology, 2022-10, Vol.52 (11), p.2131-2138 |
issn | 0301-0449 1432-1998 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9537201 |
source | SpringerLink Journals - AutoHoldings |
subjects | Algorithms Artificial intelligence Artificial Intelligence in Pediatric Radiology Automation Cardiovascular disease Children & youth Heart Imaging Machine learning Magnetic resonance imaging Medicine Medicine & Public Health Neural networks Neuroradiology Nuclear Medicine Oncology Patients Pediatrics Radiology Scanners Ultrasound |
title | The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T02%3A44%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20role%20of%20artificial%20intelligence%20in%20paediatric%20cardiovascular%20magnetic%20resonance%20imaging&rft.jtitle=Pediatric%20radiology&rft.au=Taylor,%20Andrew%20M.&rft.date=2022-10-01&rft.volume=52&rft.issue=11&rft.spage=2131&rft.epage=2138&rft.pages=2131-2138&rft.issn=0301-0449&rft.eissn=1432-1998&rft_id=info:doi/10.1007/s00247-021-05218-1&rft_dat=%3Cproquest_pubme%3E2721999436%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2721999436&rft_id=info:pmid/34936019&rfr_iscdi=true |