Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists
Background Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists. Objective The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than t...
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| Veröffentlicht in: | Pediatric radiology 2020-04, Vol.50 (4), p.516-523 |
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| creator | Reddy, Nakul E. Rayan, Jesse C. Annapragada, Ananth V. Mahmood, Nadia F. Scheslinger, Alan E. Zhang, Wei Kan, J. Herman |
| description | Background
Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.
Objective
The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.
Materials and methods
We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.
Results
The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months,
P
=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months,
P |
| doi_str_mv | 10.1007/s00247-019-04587-y |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2329729588</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2329729588</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-42b4677c06e66db3292e1472e53a473bf93324270bb9695f8a4807601753f9933</originalsourceid><addsrcrecordid>eNp9kU1v1DAQhi0EokvhD3BAlrj0Ehh_xTE3qMqHVKmXcracZLLrktiLnbTsX-BX4-0uVOqB00jzPvPO2C8hrxm8YwD6fQbgUlfATAVSNbraPSErJgWvmDHNU7ICAaxI0pyQFznfAIBQTDwnJ4I1tWBGrMjvTzEgdWukPc6YJh_c7GOgS_ZhTWMYd3TeIPWhx190KD1MH6ijId7iSN12m6LrNkfa0S6G2zguewc30oBLui_zXUw_ijhtXcKe3vl5QzfL5AJNrvdxjGuf5_ySPBvcmPHVsZ6S758vrs-_VpdXX76df7ysOqHVXEneylrrDmqs674V3HBkUnNUwkkt2sEIwSXX0LamNmponGxA18C0EoMp4ik5O_iW438umGc7-dzhOLqAccmWF0vNjWqagr59hN7EJZW37SldK9BG6ULxA9WlmHPCwW6Tn1zaWQZ2n5Q9JGVLUvY-KbsrQ2-O1ks7Yf9v5G80BRAHIBdp_-8Pu_9j-wcKD5_h</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2376507957</pqid></control><display><type>article</type><title>Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists</title><source>MEDLINE</source><source>Springer Nature - Complete Springer Journals</source><creator>Reddy, Nakul E. ; Rayan, Jesse C. ; Annapragada, Ananth V. ; Mahmood, Nadia F. ; Scheslinger, Alan E. ; Zhang, Wei ; Kan, J. Herman</creator><creatorcontrib>Reddy, Nakul E. ; Rayan, Jesse C. ; Annapragada, Ananth V. ; Mahmood, Nadia F. ; Scheslinger, Alan E. ; Zhang, Wei ; Kan, J. Herman</creatorcontrib><description>Background
Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.
Objective
The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.
Materials and methods
We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.
Results
The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months,
P
=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months,
P
<0.0001).
Conclusion
CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.</description><identifier>ISSN: 0301-0449</identifier><identifier>EISSN: 1432-1998</identifier><identifier>DOI: 10.1007/s00247-019-04587-y</identifier><identifier>PMID: 31863193</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adolescent ; Age ; Age determination ; Age Determination by Skeleton ; Artificial neural networks ; Child ; Child, Preschool ; Chronology ; Datasets ; Datasets as Topic ; Deep learning ; Female ; Finger ; Finger Phalanges - diagnostic imaging ; Ground truth ; Growth disorders ; Hand ; Humans ; Image Processing, Computer-Assisted ; Imaging ; Infant ; Male ; Medicine ; Medicine & Public Health ; Neural networks ; Neural Networks, Computer ; Neuroradiology ; Nuclear Medicine ; Oncology ; Original Article ; Pediatrics ; Physical growth ; Radiographs ; Radiography ; Radiology ; Retrospective Studies ; Ultrasound</subject><ispartof>Pediatric radiology, 2020-04, Vol.50 (4), p.516-523</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Pediatric Radiology is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-42b4677c06e66db3292e1472e53a473bf93324270bb9695f8a4807601753f9933</citedby><cites>FETCH-LOGICAL-c375t-42b4677c06e66db3292e1472e53a473bf93324270bb9695f8a4807601753f9933</cites><orcidid>0000-0001-9391-911X</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/s00247-019-04587-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00247-019-04587-y$$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/31863193$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reddy, Nakul E.</creatorcontrib><creatorcontrib>Rayan, Jesse C.</creatorcontrib><creatorcontrib>Annapragada, Ananth V.</creatorcontrib><creatorcontrib>Mahmood, Nadia F.</creatorcontrib><creatorcontrib>Scheslinger, Alan E.</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Kan, J. Herman</creatorcontrib><title>Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists</title><title>Pediatric radiology</title><addtitle>Pediatr Radiol</addtitle><addtitle>Pediatr Radiol</addtitle><description>Background
Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.
Objective
The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.
Materials and methods
We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.
Results
The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months,
P
=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months,
P
<0.0001).
Conclusion
CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.</description><subject>Adolescent</subject><subject>Age</subject><subject>Age determination</subject><subject>Age Determination by Skeleton</subject><subject>Artificial neural networks</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Chronology</subject><subject>Datasets</subject><subject>Datasets as Topic</subject><subject>Deep learning</subject><subject>Female</subject><subject>Finger</subject><subject>Finger Phalanges - diagnostic imaging</subject><subject>Ground truth</subject><subject>Growth disorders</subject><subject>Hand</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Imaging</subject><subject>Infant</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Neuroradiology</subject><subject>Nuclear Medicine</subject><subject>Oncology</subject><subject>Original Article</subject><subject>Pediatrics</subject><subject>Physical growth</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Ultrasound</subject><issn>0301-0449</issn><issn>1432-1998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU1v1DAQhi0EokvhD3BAlrj0Ehh_xTE3qMqHVKmXcracZLLrktiLnbTsX-BX4-0uVOqB00jzPvPO2C8hrxm8YwD6fQbgUlfATAVSNbraPSErJgWvmDHNU7ICAaxI0pyQFznfAIBQTDwnJ4I1tWBGrMjvTzEgdWukPc6YJh_c7GOgS_ZhTWMYd3TeIPWhx190KD1MH6ijId7iSN12m6LrNkfa0S6G2zguewc30oBLui_zXUw_ijhtXcKe3vl5QzfL5AJNrvdxjGuf5_ySPBvcmPHVsZ6S758vrs-_VpdXX76df7ysOqHVXEneylrrDmqs674V3HBkUnNUwkkt2sEIwSXX0LamNmponGxA18C0EoMp4ik5O_iW438umGc7-dzhOLqAccmWF0vNjWqagr59hN7EJZW37SldK9BG6ULxA9WlmHPCwW6Tn1zaWQZ2n5Q9JGVLUvY-KbsrQ2-O1ks7Yf9v5G80BRAHIBdp_-8Pu_9j-wcKD5_h</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Reddy, Nakul E.</creator><creator>Rayan, Jesse C.</creator><creator>Annapragada, Ananth V.</creator><creator>Mahmood, Nadia F.</creator><creator>Scheslinger, Alan E.</creator><creator>Zhang, Wei</creator><creator>Kan, J. Herman</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/0000-0001-9391-911X</orcidid></search><sort><creationdate>20200401</creationdate><title>Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists</title><author>Reddy, Nakul E. ; Rayan, Jesse C. ; Annapragada, Ananth V. ; Mahmood, Nadia F. ; Scheslinger, Alan E. ; Zhang, Wei ; Kan, J. Herman</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-42b4677c06e66db3292e1472e53a473bf93324270bb9695f8a4807601753f9933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Age</topic><topic>Age determination</topic><topic>Age Determination by Skeleton</topic><topic>Artificial neural networks</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Chronology</topic><topic>Datasets</topic><topic>Datasets as Topic</topic><topic>Deep learning</topic><topic>Female</topic><topic>Finger</topic><topic>Finger Phalanges - diagnostic imaging</topic><topic>Ground truth</topic><topic>Growth disorders</topic><topic>Hand</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Imaging</topic><topic>Infant</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Neuroradiology</topic><topic>Nuclear Medicine</topic><topic>Oncology</topic><topic>Original Article</topic><topic>Pediatrics</topic><topic>Physical growth</topic><topic>Radiographs</topic><topic>Radiography</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reddy, Nakul E.</creatorcontrib><creatorcontrib>Rayan, Jesse C.</creatorcontrib><creatorcontrib>Annapragada, Ananth V.</creatorcontrib><creatorcontrib>Mahmood, Nadia F.</creatorcontrib><creatorcontrib>Scheslinger, Alan E.</creatorcontrib><creatorcontrib>Zhang, Wei</creatorcontrib><creatorcontrib>Kan, J. Herman</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 (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</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>Reddy, Nakul E.</au><au>Rayan, Jesse C.</au><au>Annapragada, Ananth V.</au><au>Mahmood, Nadia F.</au><au>Scheslinger, Alan E.</au><au>Zhang, Wei</au><au>Kan, J. Herman</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists</atitle><jtitle>Pediatric radiology</jtitle><stitle>Pediatr Radiol</stitle><addtitle>Pediatr Radiol</addtitle><date>2020-04-01</date><risdate>2020</risdate><volume>50</volume><issue>4</issue><spage>516</spage><epage>523</epage><pages>516-523</pages><issn>0301-0449</issn><eissn>1432-1998</eissn><abstract>Background
Recently developed convolutional neural network (CNN) models determine bone age more accurately than radiologists.
Objective
The purpose of this study was to determine whether a CNN and radiologists can accurately predict bone age from radiographs using only the index finger rather than the whole hand.
Materials and methods
We used a public anonymized dataset provided by the Radiological Society of North America (RSNA) pediatric bone age challenge. The dataset contains 12,611 hand radiographs for training and 200 radiographs for testing. The index finger was cropped from these images to create a second dataset. Separate CNN models were trained using the whole-hand radiographs and the cropped second-digit dataset using the consensus ground truth provided by the RSNA bone age challenge. Bone age determination using both models was compared with ground truth as provided by the RSNA dataset. Separately, three pediatric radiologists determined bone age from the whole-hand and index-finger radiographs, and the consensus was compared to the ground truth and CNN-model-determined bone ages.
Results
The mean absolute difference between the ground truth and CNN bone age for whole-hand and index-finger was similar (4.7 months vs. 5.1 months,
P
=0.14), and both values were significantly smaller than that for radiologist bone age determination from the single-finger radiographs (8.0 months,
P
<0.0001).
Conclusion
CNN-model-determined bone ages from index-finger radiographs are similar to whole-hand bone age interpreted by radiologists in the dataset, as well as a model trained on the whole-hand radiograph. In addition, the index-finger model performed better than the ground truth compared to subspecialty trained pediatric radiologists also using only the index finger to determine bone age. The radiologist interpreting bone age can use the second digit as a reliable starting point in their search pattern.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31863193</pmid><doi>10.1007/s00247-019-04587-y</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-9391-911X</orcidid></addata></record> |
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| ispartof | Pediatric radiology, 2020-04, Vol.50 (4), p.516-523 |
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| language | eng |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Adolescent Age Age determination Age Determination by Skeleton Artificial neural networks Child Child, Preschool Chronology Datasets Datasets as Topic Deep learning Female Finger Finger Phalanges - diagnostic imaging Ground truth Growth disorders Hand Humans Image Processing, Computer-Assisted Imaging Infant Male Medicine Medicine & Public Health Neural networks Neural Networks, Computer Neuroradiology Nuclear Medicine Oncology Original Article Pediatrics Physical growth Radiographs Radiography Radiology Retrospective Studies Ultrasound |
| title | Bone age determination using only the index finger: a novel approach using a convolutional neural network compared with human radiologists |
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