Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists
Background As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists. Objective The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fract...
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| Veröffentlicht in: | Pediatric radiology 2022-10, Vol.52 (11), p.2215-2226 |
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| creator | Nguyen, Toan Maarek, Richard Hermann, Anne-Laure Kammoun, Amina Marchi, Antoine Khelifi-Touhami, Mohamed R. Collin, Mégane Jaillard, Aliénor Kompel, Andrew J. Hayashi, Daichi Guermazi, Ali Le Pointe, Hubert Ducou |
| description | Background
As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.
Objective
The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fractures by radiologists in children and young adults.
Materials and methods
A cohort of 300 anonymized radiographs performed for the detection of appendicular fractures in patients ages 2 to 21 years was collected retrospectively. The ground truth for each examination was established after an independent review by two radiologists with expertise in musculoskeletal imaging. Discrepancies were resolved by consensus with a third radiologist. Half of the 300 examinations showed at least 1 fracture. Radiographs were read by three senior pediatric radiologists and five radiology residents in the usual manner and then read again immediately after with the help of AI.
Results
The mean sensitivity for all groups was 73.3% (110/150) without AI; it increased significantly by almost 10% (
P |
| doi_str_mv | 10.1007/s00247-022-05496-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2718961596</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2721999413</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-353ec1813a1d01625898442618a3e21ddc4e8231774fe94100c5594a9d86be283</originalsourceid><addsrcrecordid>eNp9kctuEzEUhi1ERUPLC7BAltiwGerbeMbLquImVWJD1yPHPpM6TOzgYy_yNjwqTlJAYsHKlvz9n-3zE_Kas_ecseEGGRNq6JgQHeuV0Z18RlZcSdFxY8bnZMUk4x1TylySl4hbxpjsuXxBLqXm2mg9rMjPW0RA3EEsNM3URmpzCXNwwS40xALLEjYQHVAbPJ1TpuURqIcCroQUT5n9HqIPri42U_wOC5SWnbN1pWbAZqHuMSw-Q5NHTw-pxg21vi4F6fpAEWJo3uPRtp622fqQlrQJWPCaXMx2QXj1tF6Rh48fvt197u6_fvpyd3vfOTn0pZO9BMdHLi33jGvRj2ZUSmg-WgmCe-8UjELyYVAzGNXm5_reKGv8qNcgRnlF3p29-5x-VMAy7QK69n0bIVWcxMBHo3lvdEPf_oNuU82xva5Ros2--WWjxJlyOSFmmKd9DjubDxNn07G_6dzf1PqbTv1Nx9CbJ3Vd78D_ifwurAHyDGA7ihvIf-_-j_YXUBinnw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2721999413</pqid></control><display><type>article</type><title>Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Nguyen, Toan ; Maarek, Richard ; Hermann, Anne-Laure ; Kammoun, Amina ; Marchi, Antoine ; Khelifi-Touhami, Mohamed R. ; Collin, Mégane ; Jaillard, Aliénor ; Kompel, Andrew J. ; Hayashi, Daichi ; Guermazi, Ali ; Le Pointe, Hubert Ducou</creator><creatorcontrib>Nguyen, Toan ; Maarek, Richard ; Hermann, Anne-Laure ; Kammoun, Amina ; Marchi, Antoine ; Khelifi-Touhami, Mohamed R. ; Collin, Mégane ; Jaillard, Aliénor ; Kompel, Andrew J. ; Hayashi, Daichi ; Guermazi, Ali ; Le Pointe, Hubert Ducou</creatorcontrib><description>Background
As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.
Objective
The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fractures by radiologists in children and young adults.
Materials and methods
A cohort of 300 anonymized radiographs performed for the detection of appendicular fractures in patients ages 2 to 21 years was collected retrospectively. The ground truth for each examination was established after an independent review by two radiologists with expertise in musculoskeletal imaging. Discrepancies were resolved by consensus with a third radiologist. Half of the 300 examinations showed at least 1 fracture. Radiographs were read by three senior pediatric radiologists and five radiology residents in the usual manner and then read again immediately after with the help of AI.
Results
The mean sensitivity for all groups was 73.3% (110/150) without AI; it increased significantly by almost 10% (
P
<0.001) to 82.8% (125/150) with AI. For junior radiologists, it increased by 10.3% (
P
<0.001) and for senior radiologists by 8.2% (
P
=0.08). On average, there was no significant change in specificity (from 89.6% to 90.3% [+0.7%,
P
=0.28]); for junior radiologists, specificity increased from 86.2% to 87.6% (+1.4%,
P
=0.42) and for senior radiologists, it decreased from 95.1% to 94.9% (-0.2%,
P
=0.23). The stand-alone sensitivity and specificity of the AI were, respectively, 91% and 90%.
Conclusion
With the help of AI, sensitivity increased by an average of 10% without significantly decreasing specificity in fracture detection in a predominantly pediatric population.</description><identifier>ISSN: 0301-0449</identifier><identifier>EISSN: 1432-1998</identifier><identifier>DOI: 10.1007/s00247-022-05496-3</identifier><identifier>PMID: 36169667</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adolescent ; Adult ; Adults ; Artificial Intelligence ; Child ; Child, Preschool ; Children ; Emergency medical care ; Emergency medical services ; Fractures ; Fractures, Bone - diagnostic imaging ; Humans ; Imaging ; Medical diagnosis ; Medicine ; Medicine & Public Health ; Neuroradiology ; Nuclear Medicine ; Oncology ; Original Article ; Pediatrics ; Radiographs ; Radiography ; Radiologists ; Radiology ; Retrospective Studies ; Sensitivity ; Ultrasound ; Young Adult ; Young adults</subject><ispartof>Pediatric radiology, 2022-10, Vol.52 (11), p.2215-2226</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-353ec1813a1d01625898442618a3e21ddc4e8231774fe94100c5594a9d86be283</citedby><cites>FETCH-LOGICAL-c375t-353ec1813a1d01625898442618a3e21ddc4e8231774fe94100c5594a9d86be283</cites><orcidid>0000-0002-9235-445X</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/s00247-022-05496-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00247-022-05496-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36169667$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nguyen, Toan</creatorcontrib><creatorcontrib>Maarek, Richard</creatorcontrib><creatorcontrib>Hermann, Anne-Laure</creatorcontrib><creatorcontrib>Kammoun, Amina</creatorcontrib><creatorcontrib>Marchi, Antoine</creatorcontrib><creatorcontrib>Khelifi-Touhami, Mohamed R.</creatorcontrib><creatorcontrib>Collin, Mégane</creatorcontrib><creatorcontrib>Jaillard, Aliénor</creatorcontrib><creatorcontrib>Kompel, Andrew J.</creatorcontrib><creatorcontrib>Hayashi, Daichi</creatorcontrib><creatorcontrib>Guermazi, Ali</creatorcontrib><creatorcontrib>Le Pointe, Hubert Ducou</creatorcontrib><title>Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists</title><title>Pediatric radiology</title><addtitle>Pediatr Radiol</addtitle><addtitle>Pediatr Radiol</addtitle><description>Background
As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.
Objective
The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fractures by radiologists in children and young adults.
Materials and methods
A cohort of 300 anonymized radiographs performed for the detection of appendicular fractures in patients ages 2 to 21 years was collected retrospectively. The ground truth for each examination was established after an independent review by two radiologists with expertise in musculoskeletal imaging. Discrepancies were resolved by consensus with a third radiologist. Half of the 300 examinations showed at least 1 fracture. Radiographs were read by three senior pediatric radiologists and five radiology residents in the usual manner and then read again immediately after with the help of AI.
Results
The mean sensitivity for all groups was 73.3% (110/150) without AI; it increased significantly by almost 10% (
P
<0.001) to 82.8% (125/150) with AI. For junior radiologists, it increased by 10.3% (
P
<0.001) and for senior radiologists by 8.2% (
P
=0.08). On average, there was no significant change in specificity (from 89.6% to 90.3% [+0.7%,
P
=0.28]); for junior radiologists, specificity increased from 86.2% to 87.6% (+1.4%,
P
=0.42) and for senior radiologists, it decreased from 95.1% to 94.9% (-0.2%,
P
=0.23). The stand-alone sensitivity and specificity of the AI were, respectively, 91% and 90%.
Conclusion
With the help of AI, sensitivity increased by an average of 10% without significantly decreasing specificity in fracture detection in a predominantly pediatric population.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Adults</subject><subject>Artificial Intelligence</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Fractures</subject><subject>Fractures, Bone - diagnostic imaging</subject><subject>Humans</subject><subject>Imaging</subject><subject>Medical diagnosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Pediatrics</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiologists</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Sensitivity</subject><subject>Ultrasound</subject><subject>Young Adult</subject><subject>Young adults</subject><issn>0301-0449</issn><issn>1432-1998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kctuEzEUhi1ERUPLC7BAltiwGerbeMbLquImVWJD1yPHPpM6TOzgYy_yNjwqTlJAYsHKlvz9n-3zE_Kas_ecseEGGRNq6JgQHeuV0Z18RlZcSdFxY8bnZMUk4x1TylySl4hbxpjsuXxBLqXm2mg9rMjPW0RA3EEsNM3URmpzCXNwwS40xALLEjYQHVAbPJ1TpuURqIcCroQUT5n9HqIPri42U_wOC5SWnbN1pWbAZqHuMSw-Q5NHTw-pxg21vi4F6fpAEWJo3uPRtp622fqQlrQJWPCaXMx2QXj1tF6Rh48fvt197u6_fvpyd3vfOTn0pZO9BMdHLi33jGvRj2ZUSmg-WgmCe-8UjELyYVAzGNXm5_reKGv8qNcgRnlF3p29-5x-VMAy7QK69n0bIVWcxMBHo3lvdEPf_oNuU82xva5Ros2--WWjxJlyOSFmmKd9DjubDxNn07G_6dzf1PqbTv1Nx9CbJ3Vd78D_ifwurAHyDGA7ihvIf-_-j_YXUBinnw</recordid><startdate>20221001</startdate><enddate>20221001</enddate><creator>Nguyen, Toan</creator><creator>Maarek, Richard</creator><creator>Hermann, Anne-Laure</creator><creator>Kammoun, Amina</creator><creator>Marchi, Antoine</creator><creator>Khelifi-Touhami, Mohamed R.</creator><creator>Collin, Mégane</creator><creator>Jaillard, Aliénor</creator><creator>Kompel, Andrew J.</creator><creator>Hayashi, Daichi</creator><creator>Guermazi, Ali</creator><creator>Le Pointe, Hubert Ducou</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>7QP</scope><scope>7RV</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-9235-445X</orcidid></search><sort><creationdate>20221001</creationdate><title>Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists</title><author>Nguyen, Toan ; Maarek, Richard ; Hermann, Anne-Laure ; Kammoun, Amina ; Marchi, Antoine ; Khelifi-Touhami, Mohamed R. ; Collin, Mégane ; Jaillard, Aliénor ; Kompel, Andrew J. ; Hayashi, Daichi ; Guermazi, Ali ; Le Pointe, Hubert Ducou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-353ec1813a1d01625898442618a3e21ddc4e8231774fe94100c5594a9d86be283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Adults</topic><topic>Artificial Intelligence</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Children</topic><topic>Emergency medical care</topic><topic>Emergency medical services</topic><topic>Fractures</topic><topic>Fractures, Bone - diagnostic imaging</topic><topic>Humans</topic><topic>Imaging</topic><topic>Medical diagnosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Pediatrics</topic><topic>Radiographs</topic><topic>Radiography</topic><topic>Radiologists</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Sensitivity</topic><topic>Ultrasound</topic><topic>Young Adult</topic><topic>Young adults</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen, Toan</creatorcontrib><creatorcontrib>Maarek, Richard</creatorcontrib><creatorcontrib>Hermann, Anne-Laure</creatorcontrib><creatorcontrib>Kammoun, Amina</creatorcontrib><creatorcontrib>Marchi, Antoine</creatorcontrib><creatorcontrib>Khelifi-Touhami, Mohamed R.</creatorcontrib><creatorcontrib>Collin, Mégane</creatorcontrib><creatorcontrib>Jaillard, Aliénor</creatorcontrib><creatorcontrib>Kompel, Andrew J.</creatorcontrib><creatorcontrib>Hayashi, Daichi</creatorcontrib><creatorcontrib>Guermazi, Ali</creatorcontrib><creatorcontrib>Le Pointe, Hubert Ducou</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>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</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>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>Pediatric radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen, Toan</au><au>Maarek, Richard</au><au>Hermann, Anne-Laure</au><au>Kammoun, Amina</au><au>Marchi, Antoine</au><au>Khelifi-Touhami, Mohamed R.</au><au>Collin, Mégane</au><au>Jaillard, Aliénor</au><au>Kompel, Andrew J.</au><au>Hayashi, Daichi</au><au>Guermazi, Ali</au><au>Le Pointe, Hubert Ducou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists</atitle><jtitle>Pediatric radiology</jtitle><stitle>Pediatr Radiol</stitle><addtitle>Pediatr Radiol</addtitle><date>2022-10-01</date><risdate>2022</risdate><volume>52</volume><issue>11</issue><spage>2215</spage><epage>2226</epage><pages>2215-2226</pages><issn>0301-0449</issn><eissn>1432-1998</eissn><abstract>Background
As the number of conventional radiographic examinations in pediatric emergency departments increases, so, too, does the number of reading errors by radiologists.
Objective
The aim of this study is to investigate the ability of artificial intelligence (AI) to improve the detection of fractures by radiologists in children and young adults.
Materials and methods
A cohort of 300 anonymized radiographs performed for the detection of appendicular fractures in patients ages 2 to 21 years was collected retrospectively. The ground truth for each examination was established after an independent review by two radiologists with expertise in musculoskeletal imaging. Discrepancies were resolved by consensus with a third radiologist. Half of the 300 examinations showed at least 1 fracture. Radiographs were read by three senior pediatric radiologists and five radiology residents in the usual manner and then read again immediately after with the help of AI.
Results
The mean sensitivity for all groups was 73.3% (110/150) without AI; it increased significantly by almost 10% (
P
<0.001) to 82.8% (125/150) with AI. For junior radiologists, it increased by 10.3% (
P
<0.001) and for senior radiologists by 8.2% (
P
=0.08). On average, there was no significant change in specificity (from 89.6% to 90.3% [+0.7%,
P
=0.28]); for junior radiologists, specificity increased from 86.2% to 87.6% (+1.4%,
P
=0.42) and for senior radiologists, it decreased from 95.1% to 94.9% (-0.2%,
P
=0.23). The stand-alone sensitivity and specificity of the AI were, respectively, 91% and 90%.
Conclusion
With the help of AI, sensitivity increased by an average of 10% without significantly decreasing specificity in fracture detection in a predominantly pediatric population.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>36169667</pmid><doi>10.1007/s00247-022-05496-3</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-9235-445X</orcidid></addata></record> |
| fulltext | fulltext |
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| ispartof | Pediatric radiology, 2022-10, Vol.52 (11), p.2215-2226 |
| issn | 0301-0449 1432-1998 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Adolescent Adult Adults Artificial Intelligence Child Child, Preschool Children Emergency medical care Emergency medical services Fractures Fractures, Bone - diagnostic imaging Humans Imaging Medical diagnosis Medicine Medicine & Public Health Neuroradiology Nuclear Medicine Oncology Original Article Pediatrics Radiographs Radiography Radiologists Radiology Retrospective Studies Sensitivity Ultrasound Young Adult Young adults |
| title | Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists |
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