Loading…
Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review
Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and c...
Saved in:
Published in: | Imaging science in dentistry 2020, 50(2), , pp.81-92 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73 |
---|---|
cites | cdi_FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73 |
container_end_page | 92 |
container_issue | 2 |
container_start_page | 81 |
container_title | Imaging science in dentistry |
container_volume | 50 |
creator | Nagi, Ravleen Aravinda, Konidena Rakesh, N Gupta, Rajesh Pal, Ajay Mann, Amrit Kaur |
description | Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging. |
doi_str_mv | 10.5624/isd.2020.50.2.81 |
format | article |
fullrecord | <record><control><sourceid>proquest_nrf_k</sourceid><recordid>TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9478496</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2419098235</sourcerecordid><originalsourceid>FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73</originalsourceid><addsrcrecordid>eNpVUctqWzEUFKElCWn2WWrZLuzo_eiiYEzbBAKFkqyFrIerRvfqVrpO6r-vHIdAtTlzjmbmIA0AVxgtuSDsOjW_JIj0Di3JUuETcE4IpQupKHr3hgk5A5et_Ub9cKKkwKfgjBKBMFfkHOR1TmNyNkM7TbmDOZWxQTt6OIUaSx3s6AIsEaZxDjmnbRhn2PZtDkPrM-h7f1B3wWD_ppxLtC71SbU-lVy2-89wBWt4SuH5A3gfbW7h8rVegIdvX-_XN4u7H99v16u7hWNIzgsmJNbaS8kUDl56ZSnBIXqpFdcbqzHjkXImhaTKcbpxxEbhre1AxC6gF-DT0Xes0Ty6ZIpNL3VbzGM1q5_3t0YzqZgWnfvlyJ12myF4159TbTZTTYOt-xfl_zdj-tV9noykmAlEusHHV4Na_uxCm82QmutfZcdQds0QhjXSilDeqehIdbW0VkN8W4OROWRqeqbmkKnhyBCjMP0HHMWVsw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2419098235</pqid></control><display><type>article</type><title>Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review</title><source>PubMed Central Free</source><creator>Nagi, Ravleen ; Aravinda, Konidena ; Rakesh, N ; Gupta, Rajesh ; Pal, Ajay ; Mann, Amrit Kaur</creator><creatorcontrib>Nagi, Ravleen ; Aravinda, Konidena ; Rakesh, N ; Gupta, Rajesh ; Pal, Ajay ; Mann, Amrit Kaur</creatorcontrib><description>Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.</description><identifier>ISSN: 2233-7822</identifier><identifier>EISSN: 2233-7830</identifier><identifier>DOI: 10.5624/isd.2020.50.2.81</identifier><identifier>PMID: 32601582</identifier><language>eng</language><publisher>Korean Academy of Oral and Maxillofacial Radiology</publisher><subject>Review ; 치의학</subject><ispartof>Imaging Science in Dentistry, 2020, 50(2), , pp.81-92</ispartof><rights>Copyright © 2020 by Korean Academy of Oral and Maxillofacial Radiology 2020 Korean Academy of Oral and Maxillofacial Radiology</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73</citedby><cites>FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73</cites><orcidid>0000-0002-6821-3462 ; 0000-0003-4388-6334 ; 0000-0002-2480-3540 ; 0000-0001-9840-2283 ; 0000-0002-7105-4546 ; 0000-0002-0369-1383</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314602/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7314602/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002596695$$DAccess content in National Research Foundation of Korea (NRF)$$Hfree_for_read</backlink></links><search><creatorcontrib>Nagi, Ravleen</creatorcontrib><creatorcontrib>Aravinda, Konidena</creatorcontrib><creatorcontrib>Rakesh, N</creatorcontrib><creatorcontrib>Gupta, Rajesh</creatorcontrib><creatorcontrib>Pal, Ajay</creatorcontrib><creatorcontrib>Mann, Amrit Kaur</creatorcontrib><title>Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review</title><title>Imaging science in dentistry</title><description>Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.</description><subject>Review</subject><subject>치의학</subject><issn>2233-7822</issn><issn>2233-7830</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpVUctqWzEUFKElCWn2WWrZLuzo_eiiYEzbBAKFkqyFrIerRvfqVrpO6r-vHIdAtTlzjmbmIA0AVxgtuSDsOjW_JIj0Di3JUuETcE4IpQupKHr3hgk5A5et_Ub9cKKkwKfgjBKBMFfkHOR1TmNyNkM7TbmDOZWxQTt6OIUaSx3s6AIsEaZxDjmnbRhn2PZtDkPrM-h7f1B3wWD_ppxLtC71SbU-lVy2-89wBWt4SuH5A3gfbW7h8rVegIdvX-_XN4u7H99v16u7hWNIzgsmJNbaS8kUDl56ZSnBIXqpFdcbqzHjkXImhaTKcbpxxEbhre1AxC6gF-DT0Xes0Ty6ZIpNL3VbzGM1q5_3t0YzqZgWnfvlyJ12myF4159TbTZTTYOt-xfl_zdj-tV9noykmAlEusHHV4Na_uxCm82QmutfZcdQds0QhjXSilDeqehIdbW0VkN8W4OROWRqeqbmkKnhyBCjMP0HHMWVsw</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Nagi, Ravleen</creator><creator>Aravinda, Konidena</creator><creator>Rakesh, N</creator><creator>Gupta, Rajesh</creator><creator>Pal, Ajay</creator><creator>Mann, Amrit Kaur</creator><general>Korean Academy of Oral and Maxillofacial Radiology</general><general>대한영상치의학회</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>ACYCR</scope><orcidid>https://orcid.org/0000-0002-6821-3462</orcidid><orcidid>https://orcid.org/0000-0003-4388-6334</orcidid><orcidid>https://orcid.org/0000-0002-2480-3540</orcidid><orcidid>https://orcid.org/0000-0001-9840-2283</orcidid><orcidid>https://orcid.org/0000-0002-7105-4546</orcidid><orcidid>https://orcid.org/0000-0002-0369-1383</orcidid></search><sort><creationdate>20200601</creationdate><title>Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review</title><author>Nagi, Ravleen ; Aravinda, Konidena ; Rakesh, N ; Gupta, Rajesh ; Pal, Ajay ; Mann, Amrit Kaur</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Review</topic><topic>치의학</topic><toplevel>online_resources</toplevel><creatorcontrib>Nagi, Ravleen</creatorcontrib><creatorcontrib>Aravinda, Konidena</creatorcontrib><creatorcontrib>Rakesh, N</creatorcontrib><creatorcontrib>Gupta, Rajesh</creatorcontrib><creatorcontrib>Pal, Ajay</creatorcontrib><creatorcontrib>Mann, Amrit Kaur</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Korean Citation Index (Open Access)</collection><jtitle>Imaging science in dentistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nagi, Ravleen</au><au>Aravinda, Konidena</au><au>Rakesh, N</au><au>Gupta, Rajesh</au><au>Pal, Ajay</au><au>Mann, Amrit Kaur</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review</atitle><jtitle>Imaging science in dentistry</jtitle><date>2020-06-01</date><risdate>2020</risdate><volume>50</volume><issue>2</issue><spage>81</spage><epage>92</epage><pages>81-92</pages><issn>2233-7822</issn><eissn>2233-7830</eissn><abstract>Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.</abstract><pub>Korean Academy of Oral and Maxillofacial Radiology</pub><pmid>32601582</pmid><doi>10.5624/isd.2020.50.2.81</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-6821-3462</orcidid><orcidid>https://orcid.org/0000-0003-4388-6334</orcidid><orcidid>https://orcid.org/0000-0002-2480-3540</orcidid><orcidid>https://orcid.org/0000-0001-9840-2283</orcidid><orcidid>https://orcid.org/0000-0002-7105-4546</orcidid><orcidid>https://orcid.org/0000-0002-0369-1383</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2233-7822 |
ispartof | Imaging Science in Dentistry, 2020, 50(2), , pp.81-92 |
issn | 2233-7822 2233-7830 |
language | eng |
recordid | cdi_nrf_kci_oai_kci_go_kr_ARTI_9478496 |
source | PubMed Central Free |
subjects | Review 치의학 |
title | Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T20%3A07%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_nrf_k&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Clinical%20applications%20and%20performance%20of%20intelligent%20systems%20in%20dental%20and%20maxillofacial%20radiology:%20A%20review&rft.jtitle=Imaging%20science%20in%20dentistry&rft.au=Nagi,%20Ravleen&rft.date=2020-06-01&rft.volume=50&rft.issue=2&rft.spage=81&rft.epage=92&rft.pages=81-92&rft.issn=2233-7822&rft.eissn=2233-7830&rft_id=info:doi/10.5624/isd.2020.50.2.81&rft_dat=%3Cproquest_nrf_k%3E2419098235%3C/proquest_nrf_k%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c407t-467199d77481ed7d8a321efd79859ba9145f35476738c53bc2af6daabc26fed73%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2419098235&rft_id=info:pmid/32601582&rfr_iscdi=true |