Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs
Objective To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs. Methods This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a de...
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creator | Cohen, Mathieu Puntonet, Julien Sanchez, Julien Kierszbaum, Elliott Crema, Michel Soyer, Philippe Dion, Elisabeth |
description | Objective
To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs.
Methods
This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI)
Results
A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) (
p
< 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) (
p
= 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR (
p
< 0.001) and a lower specificity (92%; 95% CI: 89–95%) (
p
< 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR).
Conclusions
Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances.
Key Points
• Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice.
• Performance of artificial intelligence greatly differs depending on the anatomical area.
• Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%. |
doi_str_mv | 10.1007/s00330-022-09349-3 |
format | Article |
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To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs.
Methods
This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI)
Results
A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) (
p
< 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) (
p
= 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR (
p
< 0.001) and a lower specificity (92%; 95% CI: 89–95%) (
p
< 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR).
Conclusions
Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances.
Key Points
• Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice.
• Performance of artificial intelligence greatly differs depending on the anatomical area.
• Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-022-09349-3</identifier><identifier>PMID: 36515712</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial Intelligence ; Bones ; Diagnostic Radiology ; Fractures ; Fractures, Bone - diagnostic imaging ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Medicine ; Medicine & Public Health ; Musculoskeletal ; Neural networks ; Neuroradiology ; Radiographs ; Radiography ; Radiologists ; Radiology ; Retrospective Studies ; Scaphoid Bone ; Sensitivity ; Ultrasound ; Wrist ; Wrist Fractures ; Wrist Injuries - diagnostic imaging</subject><ispartof>European radiology, 2023-06, Vol.33 (6), p.3974-3983</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2022. 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>2022. The Author(s), under exclusive licence to European Society of Radiology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-5a82d754f1d0788fc9c15991f05a84b5b388372c503f59367d6443ffc2abbc0c3</citedby><cites>FETCH-LOGICAL-c375t-5a82d754f1d0788fc9c15991f05a84b5b388372c503f59367d6443ffc2abbc0c3</cites><orcidid>0000-0002-6566-7397</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/s00330-022-09349-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-022-09349-3$$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/36515712$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cohen, Mathieu</creatorcontrib><creatorcontrib>Puntonet, Julien</creatorcontrib><creatorcontrib>Sanchez, Julien</creatorcontrib><creatorcontrib>Kierszbaum, Elliott</creatorcontrib><creatorcontrib>Crema, Michel</creatorcontrib><creatorcontrib>Soyer, Philippe</creatorcontrib><creatorcontrib>Dion, Elisabeth</creatorcontrib><title>Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objective
To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs.
Methods
This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI)
Results
A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) (
p
< 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) (
p
= 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR (
p
< 0.001) and a lower specificity (92%; 95% CI: 89–95%) (
p
< 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR).
Conclusions
Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances.
Key Points
• Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice.
• Performance of artificial intelligence greatly differs depending on the anatomical area.
• Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Bones</subject><subject>Diagnostic Radiology</subject><subject>Fractures</subject><subject>Fractures, Bone - diagnostic imaging</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Musculoskeletal</subject><subject>Neural networks</subject><subject>Neuroradiology</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiologists</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Scaphoid Bone</subject><subject>Sensitivity</subject><subject>Ultrasound</subject><subject>Wrist</subject><subject>Wrist Fractures</subject><subject>Wrist Injuries - diagnostic imaging</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kEtPxCAUhYnROOPoH3BhSNy46XiBMlB3k4mvxMSN7kwIpTAy6bQjtBr_vWh9xYUJCXDvdw6Xg9AhgSkBEKcRgDHIgNIMCpYXGdtCY5IzmhGQ-fav8wjtxbgCgILkYheN2IwTLggdo4d56Lzzxusa-6azde2XtjEWP8cpDrrybd0ufezOsDamD9q84tbhl5BK2KVr1weLK9tZ0_m2wWl9iJZBbx7jPtpxuo724HOfoPuL87vFVXZze3m9mN9khgneZVxLWgmeO1KBkNKZwhBeFMRB6uQlL5mUTFDDgTlesJmoZnnOnDNUl6UBwyboZPDdhPapt7FTax9N-otubNtHRZM5BwpEJvT4D7pq-9Ck6RSVhEohWUESRQfKhDbGYJ3aBL_W4VURUO_ZqyF7lbJXH9krlkRHn9Z9ubbVt-Qr7ASwAYip1Sxt-Hn7H9s3I3yOwg</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Cohen, Mathieu</creator><creator>Puntonet, Julien</creator><creator>Sanchez, Julien</creator><creator>Kierszbaum, Elliott</creator><creator>Crema, Michel</creator><creator>Soyer, Philippe</creator><creator>Dion, Elisabeth</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>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6566-7397</orcidid></search><sort><creationdate>20230601</creationdate><title>Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs</title><author>Cohen, Mathieu ; Puntonet, Julien ; Sanchez, Julien ; Kierszbaum, Elliott ; Crema, Michel ; Soyer, Philippe ; Dion, Elisabeth</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-5a82d754f1d0788fc9c15991f05a84b5b388372c503f59367d6443ffc2abbc0c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Bones</topic><topic>Diagnostic Radiology</topic><topic>Fractures</topic><topic>Fractures, Bone - diagnostic imaging</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Musculoskeletal</topic><topic>Neural networks</topic><topic>Neuroradiology</topic><topic>Radiographs</topic><topic>Radiography</topic><topic>Radiologists</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Scaphoid Bone</topic><topic>Sensitivity</topic><topic>Ultrasound</topic><topic>Wrist</topic><topic>Wrist Fractures</topic><topic>Wrist Injuries - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cohen, Mathieu</creatorcontrib><creatorcontrib>Puntonet, Julien</creatorcontrib><creatorcontrib>Sanchez, Julien</creatorcontrib><creatorcontrib>Kierszbaum, Elliott</creatorcontrib><creatorcontrib>Crema, Michel</creatorcontrib><creatorcontrib>Soyer, Philippe</creatorcontrib><creatorcontrib>Dion, Elisabeth</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cohen, Mathieu</au><au>Puntonet, Julien</au><au>Sanchez, Julien</au><au>Kierszbaum, Elliott</au><au>Crema, Michel</au><au>Soyer, Philippe</au><au>Dion, Elisabeth</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2023-06-01</date><risdate>2023</risdate><volume>33</volume><issue>6</issue><spage>3974</spage><epage>3983</epage><pages>3974-3983</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objective
To compare the performances of artificial intelligence (AI) to those of radiologists in wrist fracture detection on radiographs.
Methods
This retrospective study included 637 patients (1917 radiographs) with wrist trauma between January 2017 and December 2019. The AI software used was a deep neuronal network algorithm. Ground truth was established by three senior musculoskeletal radiologists who compared the initial radiology reports (IRR) made by non-specialized radiologists, the results of AI, and the combination of AI and IRR (IR+AI)
Results
A total of 318 fractures were reported by the senior radiologists in 247 patients. Sensitivity of AI (83%; 95% CI: 78–87%) was significantly greater than that of IRR (76%; 95% CI: 70–81%) (
p
< 0.001). Specificities were similar for AI (96%; 95% CI: 93–97%) and for IRR (96%; 95% CI: 94–98%) (
p
= 0.80). The combination of AI+IRR had a significantly greater sensitivity (88%; 95% CI: 84–92%) compared to AI and IRR (
p
< 0.001) and a lower specificity (92%; 95% CI: 89–95%) (
p
< 0.001). The sensitivity for scaphoid fracture detection was acceptable for AI (84%) and IRR (80%) but poor for the detection of other carpal bones fracture (41% for AI and 26% for IRR).
Conclusions
Performance of AI in wrist fracture detection on radiographs is better than that of non-specialized radiologists. The combination of AI and radiologist’s analysis yields best performances.
Key Points
• Artificial intelligence has better performances for wrist fracture detection compared to non-expert radiologists in daily practice.
• Performance of artificial intelligence greatly differs depending on the anatomical area.
• Sensitivity of artificial intelligence for the detection of carpal bones fractures is 56%.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>36515712</pmid><doi>10.1007/s00330-022-09349-3</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6566-7397</orcidid></addata></record> |
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source | MEDLINE; Springer Online Journals Complete |
subjects | Algorithms Artificial Intelligence Bones Diagnostic Radiology Fractures Fractures, Bone - diagnostic imaging Humans Imaging Internal Medicine Interventional Radiology Medicine Medicine & Public Health Musculoskeletal Neural networks Neuroradiology Radiographs Radiography Radiologists Radiology Retrospective Studies Scaphoid Bone Sensitivity Ultrasound Wrist Wrist Fractures Wrist Injuries - diagnostic imaging |
title | Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs |
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