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AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times
•Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be h...
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| Published in: | European journal of radiology 2024-09, Vol.178, p.111638, Article 111638 |
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| container_start_page | 111638 |
| container_title | European journal of radiology |
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| creator | Peters, Sönke Kellermann, Gesa Watkinson, Joe Gärtner, Friederike Huhndorf, Monika Stürner, Klarissa Jansen, Olav Larsen, Naomi |
| description | •Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be helpful in clinical routine.
Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.
Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.
To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p |
| doi_str_mv | 10.1016/j.ejrad.2024.111638 |
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Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.
Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.
To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support).
For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.</description><identifier>ISSN: 0720-048X</identifier><identifier>ISSN: 1872-7727</identifier><identifier>EISSN: 1872-7727</identifier><identifier>DOI: 10.1016/j.ejrad.2024.111638</identifier><identifier>PMID: 39067268</identifier><language>eng</language><publisher>Ireland: Elsevier B.V</publisher><subject>Adult ; Artificial Intelligence ; Female ; FLAIR ; Humans ; Image Interpretation, Computer-Assisted - methods ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; MRI ; Multiple sclerosis ; Multiple Sclerosis - diagnostic imaging ; Reproducibility of Results ; Software ; T2-lesions ; Time Factors ; Work flow</subject><ispartof>European journal of radiology, 2024-09, Vol.178, p.111638, Article 111638</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c239t-19785fcc0c1b43732e39030119b3501d3444ae9083ff7a140e8dce8ebacdaa183</cites><orcidid>0000-0001-5151-5838</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39067268$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Peters, Sönke</creatorcontrib><creatorcontrib>Kellermann, Gesa</creatorcontrib><creatorcontrib>Watkinson, Joe</creatorcontrib><creatorcontrib>Gärtner, Friederike</creatorcontrib><creatorcontrib>Huhndorf, Monika</creatorcontrib><creatorcontrib>Stürner, Klarissa</creatorcontrib><creatorcontrib>Jansen, Olav</creatorcontrib><creatorcontrib>Larsen, Naomi</creatorcontrib><title>AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times</title><title>European journal of radiology</title><addtitle>Eur J Radiol</addtitle><description>•Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be helpful in clinical routine.
Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.
Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.
To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support).
For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.</description><subject>Adult</subject><subject>Artificial Intelligence</subject><subject>Female</subject><subject>FLAIR</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>MRI</subject><subject>Multiple sclerosis</subject><subject>Multiple Sclerosis - diagnostic imaging</subject><subject>Reproducibility of Results</subject><subject>Software</subject><subject>T2-lesions</subject><subject>Time Factors</subject><subject>Work flow</subject><issn>0720-048X</issn><issn>1872-7727</issn><issn>1872-7727</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMFu1DAQhi0EokvLEyAhH7lkGdtJ7Bw4VBUtlSpxAak3y7EnlVfOOngSJN4eL1s4cprL9_8z8zH2TsBegOg_HvZ4KC7sJch2L4TolXnBdsJo2Wgt9Uu2Ay2hgdY8XrA3RAcA6NpBvmYXaoBey97s2Hh9z2lbllxWDDzgin6N-cjzxD0WHItLfN7SGpeEnHzCkikST0iVohrwBR0h8XpJzCk_Rc8Lnuri8YmvcUa6Yq8mlwjfPs9L9v3287ebL83D17v7m-uHxks1rI0YtOkm78GLsVVaSaxXKhBiGFUHIqi2bR0OYNQ0aSdaQBM8GhydD84Joy7Zh3PvUvKPDWm1cySPKbkj5o2sAtP1ppN9X1F1Rn19hwpOdilxduWXFWBPcu3B_pFrT3LtWW5NvX9esI0zhn-ZvzYr8OkMYH3zZ8RiyUc8egyxVK825PjfBb8B92yNbg</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Peters, Sönke</creator><creator>Kellermann, Gesa</creator><creator>Watkinson, Joe</creator><creator>Gärtner, Friederike</creator><creator>Huhndorf, Monika</creator><creator>Stürner, Klarissa</creator><creator>Jansen, Olav</creator><creator>Larsen, Naomi</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5151-5838</orcidid></search><sort><creationdate>202409</creationdate><title>AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times</title><author>Peters, Sönke ; Kellermann, Gesa ; Watkinson, Joe ; Gärtner, Friederike ; Huhndorf, Monika ; Stürner, Klarissa ; Jansen, Olav ; Larsen, Naomi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c239t-19785fcc0c1b43732e39030119b3501d3444ae9083ff7a140e8dce8ebacdaa183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Artificial Intelligence</topic><topic>Female</topic><topic>FLAIR</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>MRI</topic><topic>Multiple sclerosis</topic><topic>Multiple Sclerosis - diagnostic imaging</topic><topic>Reproducibility of Results</topic><topic>Software</topic><topic>T2-lesions</topic><topic>Time Factors</topic><topic>Work flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peters, Sönke</creatorcontrib><creatorcontrib>Kellermann, Gesa</creatorcontrib><creatorcontrib>Watkinson, Joe</creatorcontrib><creatorcontrib>Gärtner, Friederike</creatorcontrib><creatorcontrib>Huhndorf, Monika</creatorcontrib><creatorcontrib>Stürner, Klarissa</creatorcontrib><creatorcontrib>Jansen, Olav</creatorcontrib><creatorcontrib>Larsen, Naomi</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>European journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peters, Sönke</au><au>Kellermann, Gesa</au><au>Watkinson, Joe</au><au>Gärtner, Friederike</au><au>Huhndorf, Monika</au><au>Stürner, Klarissa</au><au>Jansen, Olav</au><au>Larsen, Naomi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times</atitle><jtitle>European journal of radiology</jtitle><addtitle>Eur J Radiol</addtitle><date>2024-09</date><risdate>2024</risdate><volume>178</volume><spage>111638</spage><pages>111638-</pages><artnum>111638</artnum><issn>0720-048X</issn><issn>1872-7727</issn><eissn>1872-7727</eissn><abstract>•Many vendors offer commercial software for the detection of multiple sclerosis lesions in MRI.•Benefit of software support in clinical practice is not proven yet.•AI-based support for image-interpretation significantly decreases reporting times.•Usage of AI-support for image-interpretation can be helpful in clinical routine.
Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.
Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.
To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support).
For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.</abstract><cop>Ireland</cop><pub>Elsevier B.V</pub><pmid>39067268</pmid><doi>10.1016/j.ejrad.2024.111638</doi><orcidid>https://orcid.org/0000-0001-5151-5838</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Artificial Intelligence Female FLAIR Humans Image Interpretation, Computer-Assisted - methods Magnetic Resonance Imaging - methods Male Middle Aged MRI Multiple sclerosis Multiple Sclerosis - diagnostic imaging Reproducibility of Results Software T2-lesions Time Factors Work flow |
| title | AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times |
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