Validation of automated bone age analysis from hand radiographs in a North American pediatric population
Background Radiographic bone age assessment by automated software is precise and instantaneous. Objective The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment. Materials and methods We compared a total of 586 bone age radiographs from 451 patients, which ha...
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| Veröffentlicht in: | Pediatric radiology 2022-06, Vol.52 (7), p.1347-1355 |
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| container_title | Pediatric radiology |
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| creator | Bowden, Jonathan J. Bowden, Sasigarn A. Ruess, Lynne Adler, Brent H. Hu, Houchun Krishnamurthy, Rajesh Krishnamurthy, Ramkumar |
| description | Background
Radiographic bone age assessment by automated software is precise and instantaneous.
Objective
The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
Materials and methods
We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R
2
).
Results
Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R
2
=0.96;
P |
| doi_str_mv | 10.1007/s00247-022-05310-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2644007601</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2675828218</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-8cd57653368051d973ab07531fa47ada6dae66808fffce36f4312facdcfaf27b3</originalsourceid><addsrcrecordid>eNp9kcFu2zAMhoVhxZJ2e4EdCgG77OKNkizJOQZBuxYItkvbq8BYUqLCtjzJPvTtqzbtCuzQA0EC_PgT5E_IVwY_GID-mQF4rSvgvAIpGFTwgSxZLXjFVqvmI1mCAFZBXa8W5DTnewAQkolPZCGk4JIrtSSHO-yCxSnEgUZPcZ5ij5OzdBcHR3FfYsDuIYdMfYo9PeBgaUIb4j7heMg0DBTp75imA133LoUWBzo6G3AqNR3jOHfP6p_Jiccuuy8v-YzcXl7cbK6q7Z9f15v1tmqFllPVtFZqJYVQDUhmV1rgDnS5zmOt0aKy6FTpNd771gnla8G4x9a2Hj3XO3FGvh91xxT_zi5Ppg-5dV2Hg4tzNlzVdXmeAlbQb_-h93FO5dwnSsuGN5w1heJHqk0x5-S8GVPoMT0YBubJB3P0wRQfzLMPBsrQ-Yv0vOud_Tfy-vgCiCOQS2vYu_S2-x3ZR0ntk6A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2675828218</pqid></control><display><type>article</type><title>Validation of automated bone age analysis from hand radiographs in a North American pediatric population</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Bowden, Jonathan J. ; Bowden, Sasigarn A. ; Ruess, Lynne ; Adler, Brent H. ; Hu, Houchun ; Krishnamurthy, Rajesh ; Krishnamurthy, Ramkumar</creator><creatorcontrib>Bowden, Jonathan J. ; Bowden, Sasigarn A. ; Ruess, Lynne ; Adler, Brent H. ; Hu, Houchun ; Krishnamurthy, Rajesh ; Krishnamurthy, Ramkumar</creatorcontrib><description>Background
Radiographic bone age assessment by automated software is precise and instantaneous.
Objective
The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
Materials and methods
We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R
2
).
Results
Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R
2
=0.96;
P
<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (
P
<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R
2
=0.63;
P
<0.01).
Conclusion
Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.</description><identifier>ISSN: 0301-0449</identifier><identifier>EISSN: 1432-1998</identifier><identifier>DOI: 10.1007/s00247-022-05310-0</identifier><identifier>PMID: 35325266</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Age ; Age Determination by Skeleton - methods ; Artificial intelligence ; Automation ; Bone and Bones ; Child ; Error analysis ; Gender ; Growth Disorders ; Hand - diagnostic imaging ; Humans ; Imaging ; Infant ; Medicine ; Medicine & Public Health ; Neuroradiology ; Nuclear Medicine ; Oncology ; Original Article ; Patients ; Pediatrics ; Physical growth ; Puberty ; Race ; Radiographs ; Radiography ; Radiology ; Software ; Ultrasound</subject><ispartof>Pediatric radiology, 2022-06, Vol.52 (7), p.1347-1355</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022</rights><rights>2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-8cd57653368051d973ab07531fa47ada6dae66808fffce36f4312facdcfaf27b3</citedby><cites>FETCH-LOGICAL-c375t-8cd57653368051d973ab07531fa47ada6dae66808fffce36f4312facdcfaf27b3</cites><orcidid>0000-0002-0479-5213</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/s00247-022-05310-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00247-022-05310-0$$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/35325266$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bowden, Jonathan J.</creatorcontrib><creatorcontrib>Bowden, Sasigarn A.</creatorcontrib><creatorcontrib>Ruess, Lynne</creatorcontrib><creatorcontrib>Adler, Brent H.</creatorcontrib><creatorcontrib>Hu, Houchun</creatorcontrib><creatorcontrib>Krishnamurthy, Rajesh</creatorcontrib><creatorcontrib>Krishnamurthy, Ramkumar</creatorcontrib><title>Validation of automated bone age analysis from hand radiographs in a North American pediatric population</title><title>Pediatric radiology</title><addtitle>Pediatr Radiol</addtitle><addtitle>Pediatr Radiol</addtitle><description>Background
Radiographic bone age assessment by automated software is precise and instantaneous.
Objective
The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
Materials and methods
We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R
2
).
Results
Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R
2
=0.96;
P
<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (
P
<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R
2
=0.63;
P
<0.01).
Conclusion
Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.</description><subject>Age</subject><subject>Age Determination by Skeleton - methods</subject><subject>Artificial intelligence</subject><subject>Automation</subject><subject>Bone and Bones</subject><subject>Child</subject><subject>Error analysis</subject><subject>Gender</subject><subject>Growth Disorders</subject><subject>Hand - diagnostic imaging</subject><subject>Humans</subject><subject>Imaging</subject><subject>Infant</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Physical growth</subject><subject>Puberty</subject><subject>Race</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiology</subject><subject>Software</subject><subject>Ultrasound</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>eNp9kcFu2zAMhoVhxZJ2e4EdCgG77OKNkizJOQZBuxYItkvbq8BYUqLCtjzJPvTtqzbtCuzQA0EC_PgT5E_IVwY_GID-mQF4rSvgvAIpGFTwgSxZLXjFVqvmI1mCAFZBXa8W5DTnewAQkolPZCGk4JIrtSSHO-yCxSnEgUZPcZ5ij5OzdBcHR3FfYsDuIYdMfYo9PeBgaUIb4j7heMg0DBTp75imA133LoUWBzo6G3AqNR3jOHfP6p_Jiccuuy8v-YzcXl7cbK6q7Z9f15v1tmqFllPVtFZqJYVQDUhmV1rgDnS5zmOt0aKy6FTpNd771gnla8G4x9a2Hj3XO3FGvh91xxT_zi5Ppg-5dV2Hg4tzNlzVdXmeAlbQb_-h93FO5dwnSsuGN5w1heJHqk0x5-S8GVPoMT0YBubJB3P0wRQfzLMPBsrQ-Yv0vOud_Tfy-vgCiCOQS2vYu_S2-x3ZR0ntk6A</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Bowden, Jonathan J.</creator><creator>Bowden, Sasigarn A.</creator><creator>Ruess, Lynne</creator><creator>Adler, Brent H.</creator><creator>Hu, Houchun</creator><creator>Krishnamurthy, Rajesh</creator><creator>Krishnamurthy, Ramkumar</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/0000-0002-0479-5213</orcidid></search><sort><creationdate>20220601</creationdate><title>Validation of automated bone age analysis from hand radiographs in a North American pediatric population</title><author>Bowden, Jonathan J. ; Bowden, Sasigarn A. ; Ruess, Lynne ; Adler, Brent H. ; Hu, Houchun ; Krishnamurthy, Rajesh ; Krishnamurthy, Ramkumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-8cd57653368051d973ab07531fa47ada6dae66808fffce36f4312facdcfaf27b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Age</topic><topic>Age Determination by Skeleton - methods</topic><topic>Artificial intelligence</topic><topic>Automation</topic><topic>Bone and Bones</topic><topic>Child</topic><topic>Error analysis</topic><topic>Gender</topic><topic>Growth Disorders</topic><topic>Hand - diagnostic imaging</topic><topic>Humans</topic><topic>Imaging</topic><topic>Infant</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Patients</topic><topic>Pediatrics</topic><topic>Physical growth</topic><topic>Puberty</topic><topic>Race</topic><topic>Radiographs</topic><topic>Radiography</topic><topic>Radiology</topic><topic>Software</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bowden, Jonathan J.</creatorcontrib><creatorcontrib>Bowden, Sasigarn A.</creatorcontrib><creatorcontrib>Ruess, Lynne</creatorcontrib><creatorcontrib>Adler, Brent H.</creatorcontrib><creatorcontrib>Hu, Houchun</creatorcontrib><creatorcontrib>Krishnamurthy, Rajesh</creatorcontrib><creatorcontrib>Krishnamurthy, Ramkumar</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>Bowden, Jonathan J.</au><au>Bowden, Sasigarn A.</au><au>Ruess, Lynne</au><au>Adler, Brent H.</au><au>Hu, Houchun</au><au>Krishnamurthy, Rajesh</au><au>Krishnamurthy, Ramkumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Validation of automated bone age analysis from hand radiographs in a North American pediatric population</atitle><jtitle>Pediatric radiology</jtitle><stitle>Pediatr Radiol</stitle><addtitle>Pediatr Radiol</addtitle><date>2022-06-01</date><risdate>2022</risdate><volume>52</volume><issue>7</issue><spage>1347</spage><epage>1355</epage><pages>1347-1355</pages><issn>0301-0449</issn><eissn>1432-1998</eissn><abstract>Background
Radiographic bone age assessment by automated software is precise and instantaneous.
Objective
The aim of this study was to evaluate the accuracy of an automated tool for bone age assessment.
Materials and methods
We compared a total of 586 bone age radiographs from 451 patients, which had been assessed by three radiologists from 2013 to 2018, with bone age analysis by BoneXpert, using the Greulich and Pyle method. We made bone age comparisons in different patient groups based on gender, diagnosis and race, and in a subset with repeated bone age studies. We calculated Spearman correlation (r) and accuracy (root mean square error, or R
2
).
Results
Bone age analyses by automated and manual assessments showed a strong correlation (r=0.98; R
2
=0.96;
P
<0.0001), with the mean bone age difference of 0.12±0.76 years. Bone age comparisons by the two methods remained strongly correlated (
P
<0.0001) when stratified by gender, common endocrine conditions including growth disorders and early/precocious puberty, and race. In the longitudinal analysis, we also found a strong correlation between the automated software and manual bone age over time (r=0.7852; R
2
=0.63;
P
<0.01).
Conclusion
Automated bone age assessment was found to be reliable and accurate in a large cohort of pediatric patients in a clinical practice setting in North America.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35325266</pmid><doi>10.1007/s00247-022-05310-0</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-0479-5213</orcidid></addata></record> |
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| ispartof | Pediatric radiology, 2022-06, Vol.52 (7), p.1347-1355 |
| issn | 0301-0449 1432-1998 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Age Age Determination by Skeleton - methods Artificial intelligence Automation Bone and Bones Child Error analysis Gender Growth Disorders Hand - diagnostic imaging Humans Imaging Infant Medicine Medicine & Public Health Neuroradiology Nuclear Medicine Oncology Original Article Patients Pediatrics Physical growth Puberty Race Radiographs Radiography Radiology Software Ultrasound |
| title | Validation of automated bone age analysis from hand radiographs in a North American pediatric population |
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