Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images
Purpose The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time. Methods TOF-MRA clinical bra...
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| Veröffentlicht in: | Neuroradiology 2024-12, Vol.66 (12), p.2195-2204 |
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| creator | Adamchic, Ilya Kantelhardt, Sven R. Wagner, Hans-Joachim Burbelko, Michael |
| description | Purpose
The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time.
Methods
TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software.
Results
In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction).
Conclusions
Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter. |
| doi_str_mv | 10.1007/s00234-024-03460-6 |
| format | Article |
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The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time.
Methods
TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software.
Results
In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction).
Conclusions
Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.</description><identifier>ISSN: 0028-3940</identifier><identifier>ISSN: 1432-1920</identifier><identifier>EISSN: 1432-1920</identifier><identifier>DOI: 10.1007/s00234-024-03460-6</identifier><identifier>PMID: 39230716</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Aneurysm ; Aneurysms ; Artificial Intelligence ; Brain ; Diagnostic Neuroradiology ; Female ; Humans ; Image analysis ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Imaging ; Incidental Findings ; Intracranial Aneurysm - diagnostic imaging ; Magnetic Resonance Angiography - methods ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Neuroimaging ; Neurology ; Neuroradiology ; Neurosciences ; Neurosurgery ; Radiology ; Reproducibility of Results ; Sensitivity analysis ; Sensitivity and Specificity ; Software ; Software reliability ; Time measurement</subject><ispartof>Neuroradiology, 2024-12, Vol.66 (12), p.2195-2204</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>Copyright Springer Nature B.V. Dec 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c256t-8ffa5997d3aa3b18f08a082077fc05b09a41376a303564a79be7730c2f2990c83</cites><orcidid>0009-0006-1749-8230</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/s00234-024-03460-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00234-024-03460-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39230716$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Adamchic, Ilya</creatorcontrib><creatorcontrib>Kantelhardt, Sven R.</creatorcontrib><creatorcontrib>Wagner, Hans-Joachim</creatorcontrib><creatorcontrib>Burbelko, Michael</creatorcontrib><title>Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images</title><title>Neuroradiology</title><addtitle>Neuroradiology</addtitle><addtitle>Neuroradiology</addtitle><description>Purpose
The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time.
Methods
TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software.
Results
In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction).
Conclusions
Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aneurysm</subject><subject>Aneurysms</subject><subject>Artificial Intelligence</subject><subject>Brain</subject><subject>Diagnostic Neuroradiology</subject><subject>Female</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Imaging</subject><subject>Incidental Findings</subject><subject>Intracranial Aneurysm - diagnostic imaging</subject><subject>Magnetic Resonance Angiography - methods</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neuroimaging</subject><subject>Neurology</subject><subject>Neuroradiology</subject><subject>Neurosciences</subject><subject>Neurosurgery</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Sensitivity analysis</subject><subject>Sensitivity and Specificity</subject><subject>Software</subject><subject>Software reliability</subject><subject>Time measurement</subject><issn>0028-3940</issn><issn>1432-1920</issn><issn>1432-1920</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kcFu1DAQhi0EokvhBTggS1y4BCZ2YsfHVUWhUlElVM6R44y3rhJnaydI-1y8IBNSQOLAYWRZ8_2_Z_wz9rqE9yWA_pABhKwKEFSyUlCoJ2xXVlIUpRHwlO2o3xTSVHDGXuR8DwBSS_2cnUkjJOhS7diPfZqDDy7YgYc44zCEA0aH3NnI73A48h5ndHOIB-q70GOcNzRZl2xcdTbikk555FPkaVqIRd4lGyL_8vWKL3nV3t5c0m3PeztbnnHOpOp5GI9p-o58vqMKI_KED0tI2HM_JSLscMoh88mvREbi7QHzS_bM2yHjq8fznH27_Hh78bm4vvl0dbG_Lpyo1Vw03tvaGN1La2VXNh4aC40Arb2DugNjq1JqZSXIWlVWmw61luCEF8aAa-Q5e7f50pAPC-a5HUN29EW08LTkVlIKtaqFUoS-_Qe9n5ZE86-UrExTN6okSmyUS1POCX17TLRSOrUltGuk7RZpS5G2vyJtV-s3j9ZLN2L_R_I7QwLkBmRqxQOmv2__x_YnkhWtmA</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Adamchic, Ilya</creator><creator>Kantelhardt, Sven R.</creator><creator>Wagner, Hans-Joachim</creator><creator>Burbelko, Michael</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>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><orcidid>https://orcid.org/0009-0006-1749-8230</orcidid></search><sort><creationdate>20241201</creationdate><title>Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images</title><author>Adamchic, Ilya ; Kantelhardt, Sven R. ; Wagner, Hans-Joachim ; Burbelko, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c256t-8ffa5997d3aa3b18f08a082077fc05b09a41376a303564a79be7730c2f2990c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aneurysm</topic><topic>Aneurysms</topic><topic>Artificial Intelligence</topic><topic>Brain</topic><topic>Diagnostic Neuroradiology</topic><topic>Female</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image processing</topic><topic>Imaging</topic><topic>Incidental Findings</topic><topic>Intracranial Aneurysm - diagnostic imaging</topic><topic>Magnetic Resonance Angiography - methods</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neuroimaging</topic><topic>Neurology</topic><topic>Neuroradiology</topic><topic>Neurosciences</topic><topic>Neurosurgery</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Sensitivity analysis</topic><topic>Sensitivity and Specificity</topic><topic>Software</topic><topic>Software reliability</topic><topic>Time measurement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adamchic, Ilya</creatorcontrib><creatorcontrib>Kantelhardt, Sven R.</creatorcontrib><creatorcontrib>Wagner, Hans-Joachim</creatorcontrib><creatorcontrib>Burbelko, Michael</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</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><jtitle>Neuroradiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adamchic, Ilya</au><au>Kantelhardt, Sven R.</au><au>Wagner, Hans-Joachim</au><au>Burbelko, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images</atitle><jtitle>Neuroradiology</jtitle><stitle>Neuroradiology</stitle><addtitle>Neuroradiology</addtitle><date>2024-12-01</date><risdate>2024</risdate><volume>66</volume><issue>12</issue><spage>2195</spage><epage>2204</epage><pages>2195-2204</pages><issn>0028-3940</issn><issn>1432-1920</issn><eissn>1432-1920</eissn><abstract>Purpose
The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time.
Methods
TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software.
Results
In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction).
Conclusions
Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>39230716</pmid><doi>10.1007/s00234-024-03460-6</doi><tpages>10</tpages><orcidid>https://orcid.org/0009-0006-1749-8230</orcidid></addata></record> |
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| subjects | Adult Aged Aged, 80 and over Aneurysm Aneurysms Artificial Intelligence Brain Diagnostic Neuroradiology Female Humans Image analysis Image Interpretation, Computer-Assisted - methods Image processing Imaging Incidental Findings Intracranial Aneurysm - diagnostic imaging Magnetic Resonance Angiography - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medicine Medicine & Public Health Middle Aged Neuroimaging Neurology Neuroradiology Neurosciences Neurosurgery Radiology Reproducibility of Results Sensitivity analysis Sensitivity and Specificity Software Software reliability Time measurement |
| title | Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images |
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