AI for Calcium Scoring
Calcium scoring, a process in which arterial calcifications are detected and quantified in CT, is valuable in estimating the risk of cardiovascular disease events. Especially when used to quantify the extent of calcification in the coronary arteries, it is a strong and independent predictor of coron...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | van Velzen, Sanne G. M Hampe, Nils de Vos, Bob D Išgum, Ivana |
| description | Calcium scoring, a process in which arterial calcifications are detected and
quantified in CT, is valuable in estimating the risk of cardiovascular disease
events. Especially when used to quantify the extent of calcification in the
coronary arteries, it is a strong and independent predictor of coronary heart
disease events. Advances in artificial intelligence (AI)-based image analysis
have produced a multitude of automatic calcium scoring methods. While most
early methods closely follow standard calcium scoring accepted in clinic,
recent approaches extend this procedure to enable faster or more reproducible
calcium scoring. This chapter provides an introduction to AI for calcium
scoring, and an overview of the developed methods and their applications. We
conclude with a discussion on AI methods in calcium scoring and propose
potential directions for future research. |
| doi_str_mv | 10.48550/arxiv.2105.12558 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2105_12558</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2105_12558</sourcerecordid><originalsourceid>FETCH-LOGICAL-a678-83eecb934e4526df484e1f9b92c9943860b79e53bb73db9f52d6efd96343c18d3</originalsourceid><addsrcrecordid>eNotzrsKwjAYQOEsDlJdBSf7Aq1N_iRNxlK8FAoOdi-5SqG1ElH07cXqdLbDh9AaZykVjGVbFV7dMyU4YykmjIk5WhVV7McQl6o33WOIz2YM3fWyQDOv-rtb_huhZr9rymNSnw5VWdSJ4rlIBDhntATqKCPceiqow15qSYyUFATPdC4dA61zsFp6Rix33koOFAwWFiK0-W0nWHsL3aDCu_0C2wkIH5L3M44</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>AI for Calcium Scoring</title><source>arXiv.org</source><creator>van Velzen, Sanne G. M ; Hampe, Nils ; de Vos, Bob D ; Išgum, Ivana</creator><creatorcontrib>van Velzen, Sanne G. M ; Hampe, Nils ; de Vos, Bob D ; Išgum, Ivana</creatorcontrib><description>Calcium scoring, a process in which arterial calcifications are detected and
quantified in CT, is valuable in estimating the risk of cardiovascular disease
events. Especially when used to quantify the extent of calcification in the
coronary arteries, it is a strong and independent predictor of coronary heart
disease events. Advances in artificial intelligence (AI)-based image analysis
have produced a multitude of automatic calcium scoring methods. While most
early methods closely follow standard calcium scoring accepted in clinic,
recent approaches extend this procedure to enable faster or more reproducible
calcium scoring. This chapter provides an introduction to AI for calcium
scoring, and an overview of the developed methods and their applications. We
conclude with a discussion on AI methods in calcium scoring and propose
potential directions for future research.</description><identifier>DOI: 10.48550/arxiv.2105.12558</identifier><language>eng</language><creationdate>2021-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2105.12558$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2105.12558$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>van Velzen, Sanne G. M</creatorcontrib><creatorcontrib>Hampe, Nils</creatorcontrib><creatorcontrib>de Vos, Bob D</creatorcontrib><creatorcontrib>Išgum, Ivana</creatorcontrib><title>AI for Calcium Scoring</title><description>Calcium scoring, a process in which arterial calcifications are detected and
quantified in CT, is valuable in estimating the risk of cardiovascular disease
events. Especially when used to quantify the extent of calcification in the
coronary arteries, it is a strong and independent predictor of coronary heart
disease events. Advances in artificial intelligence (AI)-based image analysis
have produced a multitude of automatic calcium scoring methods. While most
early methods closely follow standard calcium scoring accepted in clinic,
recent approaches extend this procedure to enable faster or more reproducible
calcium scoring. This chapter provides an introduction to AI for calcium
scoring, and an overview of the developed methods and their applications. We
conclude with a discussion on AI methods in calcium scoring and propose
potential directions for future research.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrsKwjAYQOEsDlJdBSf7Aq1N_iRNxlK8FAoOdi-5SqG1ElH07cXqdLbDh9AaZykVjGVbFV7dMyU4YykmjIk5WhVV7McQl6o33WOIz2YM3fWyQDOv-rtb_huhZr9rymNSnw5VWdSJ4rlIBDhntATqKCPceiqow15qSYyUFATPdC4dA61zsFp6Rix33koOFAwWFiK0-W0nWHsL3aDCu_0C2wkIH5L3M44</recordid><startdate>20210524</startdate><enddate>20210524</enddate><creator>van Velzen, Sanne G. M</creator><creator>Hampe, Nils</creator><creator>de Vos, Bob D</creator><creator>Išgum, Ivana</creator><scope>GOX</scope></search><sort><creationdate>20210524</creationdate><title>AI for Calcium Scoring</title><author>van Velzen, Sanne G. M ; Hampe, Nils ; de Vos, Bob D ; Išgum, Ivana</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-83eecb934e4526df484e1f9b92c9943860b79e53bb73db9f52d6efd96343c18d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><toplevel>online_resources</toplevel><creatorcontrib>van Velzen, Sanne G. M</creatorcontrib><creatorcontrib>Hampe, Nils</creatorcontrib><creatorcontrib>de Vos, Bob D</creatorcontrib><creatorcontrib>Išgum, Ivana</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>van Velzen, Sanne G. M</au><au>Hampe, Nils</au><au>de Vos, Bob D</au><au>Išgum, Ivana</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AI for Calcium Scoring</atitle><date>2021-05-24</date><risdate>2021</risdate><abstract>Calcium scoring, a process in which arterial calcifications are detected and
quantified in CT, is valuable in estimating the risk of cardiovascular disease
events. Especially when used to quantify the extent of calcification in the
coronary arteries, it is a strong and independent predictor of coronary heart
disease events. Advances in artificial intelligence (AI)-based image analysis
have produced a multitude of automatic calcium scoring methods. While most
early methods closely follow standard calcium scoring accepted in clinic,
recent approaches extend this procedure to enable faster or more reproducible
calcium scoring. This chapter provides an introduction to AI for calcium
scoring, and an overview of the developed methods and their applications. We
conclude with a discussion on AI methods in calcium scoring and propose
potential directions for future research.</abstract><doi>10.48550/arxiv.2105.12558</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2105.12558 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2105_12558 |
| source | arXiv.org |
| title | AI for Calcium Scoring |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T13%3A14%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=AI%20for%20Calcium%20Scoring&rft.au=van%20Velzen,%20Sanne%20G.%20M&rft.date=2021-05-24&rft_id=info:doi/10.48550/arxiv.2105.12558&rft_dat=%3Carxiv_GOX%3E2105_12558%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |