Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software
Purpose The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three cente...
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creator | Federau, Christian Hainc, Nicolin Edjlali, Myriam Zhu, Guangming Mastilovic, Milica Nierobisch, Nathalie Uhlemann, Jan-Philipp Paganucci, Silvio Granziera, Cristina Heinzlef, Olivier Kipp, Lucas B. Wintermark, Max |
description | Purpose
The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.
Methods
In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.
Results
A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).
Conclusion
The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz. |
doi_str_mv | 10.1007/s00234-024-03293-3 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10859335</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918197421</sourcerecordid><originalsourceid>FETCH-LOGICAL-c426t-f59141b8579273d30f6f0e638306132da99302e3821e5d972f9a13980bc2a8fd3</originalsourceid><addsrcrecordid>eNp9kctuFDEQRS0EIkPgB1ggS2yyabBd_bBXCEUBIgUhIVhbnm6748htT_yYKD_Dt-JJh_BYsLCsqjp1XeWL0EtK3lBChreJEAZtQ1g9wAQ08AhtaAusoYKRx2hT67wB0ZIj9CylK0IIDDA8RUfAWd_1vN2gH2d75YrKNngcDM6XGl8X5Wy-xcpPd_EuhqmM2e4Pycp4XWKIarLBhdmOyuGoa-TnQ3EpLtud0ziNTseQbMImOBdumrLDn7-e17zyCZd04JXH1mftnJ21z1iVHJZ1lBRMvlFRP0dPjHJJv7i_j9H3D2ffTj81F18-np--v2jGlvW5MZ2gLd3ybhBsgAmI6Q3RPXAgPQU2KSGAMF3XprqbxMCMUBQEJ9uRKW4mOEbvVt1d2S56Gus4UTm5i3ZR8VYGZeXfFW8v5Rz2khLeCYCuKpzcK8RwXXTKcrFprLspr0NJkgnKqRhaRiv6-h_0KpTo636VYi0nXR27UmylxvqNKWrzMA0l8uC_XP2X1X9557-E2vTqzz0eWn4ZXgFYgVRLftbx99v_kf0JE1--8A</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2924805306</pqid></control><display><type>article</type><title>Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software</title><source>SpringerLink Journals - AutoHoldings</source><creator>Federau, Christian ; Hainc, Nicolin ; Edjlali, Myriam ; Zhu, Guangming ; Mastilovic, Milica ; Nierobisch, Nathalie ; Uhlemann, Jan-Philipp ; Paganucci, Silvio ; Granziera, Cristina ; Heinzlef, Olivier ; Kipp, Lucas B. ; Wintermark, Max</creator><creatorcontrib>Federau, Christian ; Hainc, Nicolin ; Edjlali, Myriam ; Zhu, Guangming ; Mastilovic, Milica ; Nierobisch, Nathalie ; Uhlemann, Jan-Philipp ; Paganucci, Silvio ; Granziera, Cristina ; Heinzlef, Olivier ; Kipp, Lucas B. ; Wintermark, Max</creatorcontrib><description>Purpose
The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.
Methods
In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.
Results
A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).
Conclusion
The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.</description><identifier>ISSN: 0028-3940</identifier><identifier>EISSN: 1432-1920</identifier><identifier>DOI: 10.1007/s00234-024-03293-3</identifier><identifier>PMID: 38265684</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial intelligence in neuroradiology ; Automation ; Diagnostic Neuroradiology ; Gadolinium ; Imaging ; Language ; Lesions ; Magnetic resonance imaging ; Medicine ; Medicine & Public Health ; Multiple sclerosis ; Neurology ; Neuroradiology ; Neurosciences ; Neurosurgery ; Productivity ; Radiology ; Software</subject><ispartof>Neuroradiology, 2024-03, Vol.66 (3), p.361-369</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. 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><cites>FETCH-LOGICAL-c426t-f59141b8579273d30f6f0e638306132da99302e3821e5d972f9a13980bc2a8fd3</cites><orcidid>0000-0002-3803-6602</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00234-024-03293-3$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00234-024-03293-3$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38265684$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Federau, Christian</creatorcontrib><creatorcontrib>Hainc, Nicolin</creatorcontrib><creatorcontrib>Edjlali, Myriam</creatorcontrib><creatorcontrib>Zhu, Guangming</creatorcontrib><creatorcontrib>Mastilovic, Milica</creatorcontrib><creatorcontrib>Nierobisch, Nathalie</creatorcontrib><creatorcontrib>Uhlemann, Jan-Philipp</creatorcontrib><creatorcontrib>Paganucci, Silvio</creatorcontrib><creatorcontrib>Granziera, Cristina</creatorcontrib><creatorcontrib>Heinzlef, Olivier</creatorcontrib><creatorcontrib>Kipp, Lucas B.</creatorcontrib><creatorcontrib>Wintermark, Max</creatorcontrib><title>Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software</title><title>Neuroradiology</title><addtitle>Neuroradiology</addtitle><addtitle>Neuroradiology</addtitle><description>Purpose
The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.
Methods
In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.
Results
A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).
Conclusion
The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.</description><subject>Artificial intelligence in neuroradiology</subject><subject>Automation</subject><subject>Diagnostic Neuroradiology</subject><subject>Gadolinium</subject><subject>Imaging</subject><subject>Language</subject><subject>Lesions</subject><subject>Magnetic resonance imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Multiple sclerosis</subject><subject>Neurology</subject><subject>Neuroradiology</subject><subject>Neurosciences</subject><subject>Neurosurgery</subject><subject>Productivity</subject><subject>Radiology</subject><subject>Software</subject><issn>0028-3940</issn><issn>1432-1920</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kctuFDEQRS0EIkPgB1ggS2yyabBd_bBXCEUBIgUhIVhbnm6748htT_yYKD_Dt-JJh_BYsLCsqjp1XeWL0EtK3lBChreJEAZtQ1g9wAQ08AhtaAusoYKRx2hT67wB0ZIj9CylK0IIDDA8RUfAWd_1vN2gH2d75YrKNngcDM6XGl8X5Wy-xcpPd_EuhqmM2e4Pycp4XWKIarLBhdmOyuGoa-TnQ3EpLtud0ziNTseQbMImOBdumrLDn7-e17zyCZd04JXH1mftnJ21z1iVHJZ1lBRMvlFRP0dPjHJJv7i_j9H3D2ffTj81F18-np--v2jGlvW5MZ2gLd3ybhBsgAmI6Q3RPXAgPQU2KSGAMF3XprqbxMCMUBQEJ9uRKW4mOEbvVt1d2S56Gus4UTm5i3ZR8VYGZeXfFW8v5Rz2khLeCYCuKpzcK8RwXXTKcrFprLspr0NJkgnKqRhaRiv6-h_0KpTo636VYi0nXR27UmylxvqNKWrzMA0l8uC_XP2X1X9557-E2vTqzz0eWn4ZXgFYgVRLftbx99v_kf0JE1--8A</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Federau, Christian</creator><creator>Hainc, Nicolin</creator><creator>Edjlali, Myriam</creator><creator>Zhu, Guangming</creator><creator>Mastilovic, Milica</creator><creator>Nierobisch, Nathalie</creator><creator>Uhlemann, Jan-Philipp</creator><creator>Paganucci, Silvio</creator><creator>Granziera, Cristina</creator><creator>Heinzlef, Olivier</creator><creator>Kipp, Lucas B.</creator><creator>Wintermark, Max</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>7QO</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3803-6602</orcidid></search><sort><creationdate>20240301</creationdate><title>Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software</title><author>Federau, Christian ; Hainc, Nicolin ; Edjlali, Myriam ; Zhu, Guangming ; Mastilovic, Milica ; Nierobisch, Nathalie ; Uhlemann, Jan-Philipp ; Paganucci, Silvio ; Granziera, Cristina ; Heinzlef, Olivier ; Kipp, Lucas B. ; Wintermark, Max</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-f59141b8579273d30f6f0e638306132da99302e3821e5d972f9a13980bc2a8fd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial intelligence in neuroradiology</topic><topic>Automation</topic><topic>Diagnostic Neuroradiology</topic><topic>Gadolinium</topic><topic>Imaging</topic><topic>Language</topic><topic>Lesions</topic><topic>Magnetic resonance imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Multiple sclerosis</topic><topic>Neurology</topic><topic>Neuroradiology</topic><topic>Neurosciences</topic><topic>Neurosurgery</topic><topic>Productivity</topic><topic>Radiology</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Federau, Christian</creatorcontrib><creatorcontrib>Hainc, Nicolin</creatorcontrib><creatorcontrib>Edjlali, Myriam</creatorcontrib><creatorcontrib>Zhu, Guangming</creatorcontrib><creatorcontrib>Mastilovic, Milica</creatorcontrib><creatorcontrib>Nierobisch, Nathalie</creatorcontrib><creatorcontrib>Uhlemann, Jan-Philipp</creatorcontrib><creatorcontrib>Paganucci, Silvio</creatorcontrib><creatorcontrib>Granziera, Cristina</creatorcontrib><creatorcontrib>Heinzlef, Olivier</creatorcontrib><creatorcontrib>Kipp, Lucas B.</creatorcontrib><creatorcontrib>Wintermark, Max</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Neuroradiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Federau, Christian</au><au>Hainc, Nicolin</au><au>Edjlali, Myriam</au><au>Zhu, Guangming</au><au>Mastilovic, Milica</au><au>Nierobisch, Nathalie</au><au>Uhlemann, Jan-Philipp</au><au>Paganucci, Silvio</au><au>Granziera, Cristina</au><au>Heinzlef, Olivier</au><au>Kipp, Lucas B.</au><au>Wintermark, Max</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software</atitle><jtitle>Neuroradiology</jtitle><stitle>Neuroradiology</stitle><addtitle>Neuroradiology</addtitle><date>2024-03-01</date><risdate>2024</risdate><volume>66</volume><issue>3</issue><spage>361</spage><epage>369</epage><pages>361-369</pages><issn>0028-3940</issn><eissn>1432-1920</eissn><abstract>Purpose
The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.
Methods
In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.
Results
A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).
Conclusion
The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>38265684</pmid><doi>10.1007/s00234-024-03293-3</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-3803-6602</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Artificial intelligence in neuroradiology Automation Diagnostic Neuroradiology Gadolinium Imaging Language Lesions Magnetic resonance imaging Medicine Medicine & Public Health Multiple sclerosis Neurology Neuroradiology Neurosciences Neurosurgery Productivity Radiology Software |
title | Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software |
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