Artificial Intelligence, Radiology, and Tuberculosis: A Review

Artificial Intelligence, Radiology, and Tuberculosis: A Review

Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridg...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Academic radiology 2020-01, Vol.27 (1), p.71-75
Hauptverfasser: Kulkarni, Sagar, Jha, Saurabh
Format: Artikel
Sprache:eng
Schlagworte:
Artificial intelligence
Computer-aided diagnosis
Deep learning
Global health
Tuberculosis
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 75
container_issue 1
container_start_page 71
container_title Academic radiology
container_volume 27
creator Kulkarni, Sagar
Jha, Saurabh
description Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.
doi_str_mv 10.1016/j.acra.2019.10.003
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2317971611</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1076633219304842</els_id><sourcerecordid>2317971611</sourcerecordid><originalsourceid>FETCH-LOGICAL-c356t-8a99422d35c97fa33af402cb6515d7c93e14d95700270a9cf89940a56dd89a73</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMobk7_gBfSSy_Wmo8maUSEMvwYDISx-5Al6cjo2pm0yv69KZteepXD4TkveR8AbhHMEETsYZsp7VWGIRJxkUFIzsAYFbxIc5iz8zhDzlJGCB6BqxC2ECLKCnIJRgRxKrhgY_Bc-s5VTjtVJ_Oms3XtNrbRdposlXFt3W4O00Q1Jln1a-t1X7fBhcekTJb2y9nva3BRqTrYm9M7AavXl9XsPV18vM1n5SLVhLIuLZQQOcaGUC14pQhRVQ6xXjOKqOFaEItyIyiHEHOohK6KyENFmTGFUJxMwP0xdu_bz96GTu5c0PGzqrFtHySOhQRHDKGI4iOqfRuCt5Xce7dT_iARlIM2uZWDNjloG3ZRWzy6O-X36501fye_niLwdARsLBmLexm0GzwZ563upGndf_k_AFh8Tg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2317971611</pqid></control><display><type>article</type><title>Artificial Intelligence, Radiology, and Tuberculosis: A Review</title><source>Elsevier ScienceDirect Journals</source><creator>Kulkarni, Sagar ; Jha, Saurabh</creator><creatorcontrib>Kulkarni, Sagar ; Jha, Saurabh</creatorcontrib><description>Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.</description><identifier>ISSN: 1076-6332</identifier><identifier>EISSN: 1878-4046</identifier><identifier>DOI: 10.1016/j.acra.2019.10.003</identifier><identifier>PMID: 31759796</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Artificial intelligence ; Computer-aided diagnosis ; Deep learning ; Global health ; Tuberculosis</subject><ispartof>Academic radiology, 2020-01, Vol.27 (1), p.71-75</ispartof><rights>2019 The Association of University Radiologists</rights><rights>Copyright © 2019 The Association of University Radiologists. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-8a99422d35c97fa33af402cb6515d7c93e14d95700270a9cf89940a56dd89a73</citedby><cites>FETCH-LOGICAL-c356t-8a99422d35c97fa33af402cb6515d7c93e14d95700270a9cf89940a56dd89a73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1076633219304842$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31759796$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kulkarni, Sagar</creatorcontrib><creatorcontrib>Jha, Saurabh</creatorcontrib><title>Artificial Intelligence, Radiology, and Tuberculosis: A Review</title><title>Academic radiology</title><addtitle>Acad Radiol</addtitle><description>Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.</description><subject>Artificial intelligence</subject><subject>Computer-aided diagnosis</subject><subject>Deep learning</subject><subject>Global health</subject><subject>Tuberculosis</subject><issn>1076-6332</issn><issn>1878-4046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMobk7_gBfSSy_Wmo8maUSEMvwYDISx-5Al6cjo2pm0yv69KZteepXD4TkveR8AbhHMEETsYZsp7VWGIRJxkUFIzsAYFbxIc5iz8zhDzlJGCB6BqxC2ECLKCnIJRgRxKrhgY_Bc-s5VTjtVJ_Oms3XtNrbRdposlXFt3W4O00Q1Jln1a-t1X7fBhcekTJb2y9nva3BRqTrYm9M7AavXl9XsPV18vM1n5SLVhLIuLZQQOcaGUC14pQhRVQ6xXjOKqOFaEItyIyiHEHOohK6KyENFmTGFUJxMwP0xdu_bz96GTu5c0PGzqrFtHySOhQRHDKGI4iOqfRuCt5Xce7dT_iARlIM2uZWDNjloG3ZRWzy6O-X36501fye_niLwdARsLBmLexm0GzwZ563upGndf_k_AFh8Tg</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Kulkarni, Sagar</creator><creator>Jha, Saurabh</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202001</creationdate><title>Artificial Intelligence, Radiology, and Tuberculosis: A Review</title><author>Kulkarni, Sagar ; Jha, Saurabh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-8a99422d35c97fa33af402cb6515d7c93e14d95700270a9cf89940a56dd89a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial intelligence</topic><topic>Computer-aided diagnosis</topic><topic>Deep learning</topic><topic>Global health</topic><topic>Tuberculosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kulkarni, Sagar</creatorcontrib><creatorcontrib>Jha, Saurabh</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Academic radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kulkarni, Sagar</au><au>Jha, Saurabh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence, Radiology, and Tuberculosis: A Review</atitle><jtitle>Academic radiology</jtitle><addtitle>Acad Radiol</addtitle><date>2020-01</date><risdate>2020</risdate><volume>27</volume><issue>1</issue><spage>71</spage><epage>75</epage><pages>71-75</pages><issn>1076-6332</issn><eissn>1878-4046</eissn><abstract>Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>31759796</pmid><doi>10.1016/j.acra.2019.10.003</doi><tpages>5</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1076-6332
ispartof Academic radiology, 2020-01, Vol.27 (1), p.71-75
issn 1076-6332
1878-4046
language eng
recordid cdi_proquest_miscellaneous_2317971611
source Elsevier ScienceDirect Journals
subjects Artificial intelligence
Computer-aided diagnosis
Deep learning
Global health
Tuberculosis
title Artificial Intelligence, Radiology, and Tuberculosis: A Review
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T07%3A33%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Artificial%20Intelligence,%20Radiology,%20and%20Tuberculosis:%20A%20Review&rft.jtitle=Academic%20radiology&rft.au=Kulkarni,%20Sagar&rft.date=2020-01&rft.volume=27&rft.issue=1&rft.spage=71&rft.epage=75&rft.pages=71-75&rft.issn=1076-6332&rft.eissn=1878-4046&rft_id=info:doi/10.1016/j.acra.2019.10.003&rft_dat=%3Cproquest_cross%3E2317971611%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2317971611&rft_id=info:pmid/31759796&rft_els_id=S1076633219304842&rfr_iscdi=true