Bone Age Estimation of Pediatrics by Analyzing Hand X-Rays Using Deep Learning Technique
The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study pres...
Saved in:
Published in | 2023 International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS) pp. 245 - 249 |
---|---|
Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
06.11.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study presents a completely automated deep learning technique for correctly determining bone age from X-ray images of the hand. The dataset used for training and evaluation is derived from the Radiological Society of North America's 2017 Pediatric Bone Age Challenge, which comprises left hand X-ray pictures annotated with gender and age information. To determine bone age precisely, we use a transfer learning technique using the pre-trained Xception model. By fine-tuning the neural network on the bone age dataset, it was possible to capture complicated patterns and attributes peculiar to bone development. With a mean absolute error (MAE) of 1.52 months, the experimental results show a remarkable level of convergence between the predicted age and the actual chronological age. The therapeutic significance of our suggested technique arises from its potential to be a beneficial tool to assist medical practitioners in more correctly and effectively determining bone age. The incorporation of AI-based autonomous bone age assessment can help reduce the diagnostic process and assist in early identification of developmental anomalies, ultimately contributing to improved pediatric healthcare. |
---|---|
AbstractList | The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate of structural and biological development, which frequently differs compared to the chronological age determined at birth. This study presents a completely automated deep learning technique for correctly determining bone age from X-ray images of the hand. The dataset used for training and evaluation is derived from the Radiological Society of North America's 2017 Pediatric Bone Age Challenge, which comprises left hand X-ray pictures annotated with gender and age information. To determine bone age precisely, we use a transfer learning technique using the pre-trained Xception model. By fine-tuning the neural network on the bone age dataset, it was possible to capture complicated patterns and attributes peculiar to bone development. With a mean absolute error (MAE) of 1.52 months, the experimental results show a remarkable level of convergence between the predicted age and the actual chronological age. The therapeutic significance of our suggested technique arises from its potential to be a beneficial tool to assist medical practitioners in more correctly and effectively determining bone age. The incorporation of AI-based autonomous bone age assessment can help reduce the diagnostic process and assist in early identification of developmental anomalies, ultimately contributing to improved pediatric healthcare. |
Author | Medikonda, Jeevan Shanbhog, Sharisha Palkar, Anisha |
Author_xml | – sequence: 1 givenname: Anisha surname: Palkar fullname: Palkar, Anisha email: anishapalkar24@gmail.com organization: Manipal Institute of Technology,Department of Biomedical Engineering,Manipal,India – sequence: 2 givenname: Sharisha orcidid: 0000-0001-7890-2416 surname: Shanbhog fullname: Shanbhog, Sharisha organization: Manipal Institute of Technology,Department of Biomedical Engineering,Manipal,India – sequence: 3 givenname: Jeevan orcidid: 0000-0003-2271-3602 surname: Medikonda fullname: Medikonda, Jeevan organization: Manipal Institute of Technology,Department of Biomedical Engineering,Manipal,India |
BookMark | eNo1j81OwkAUhcdEF4q8gYvxAYpzO__LiigkTTQICTsyM73FSXDAti7q01Oirk7OSb6TfDfkMh0SEnIPbALA7MNiuiwW79IqIyY5y_kEGFcaQFyQsdXWcMk4U4rra7J5HFBa7JDO2i5-ui4eEj3U9A2r6Lomhpb6nhbJ7fufmHZ07lJFN9nS9S1dt-flCfFIS3RNOrcVho8Uv77xllzVbt_i-C9HZP08W03nWfn6spgWZRYBbJdpwSrPqtryGupKGgU6CGsMBC-lkJoHj8EHkysrhOUcwataehhktDHa8xG5-_2NiLg9NoND02__hfkJgytPtw |
ContentType | Conference Proceeding |
DBID | 6IE 6IL CBEJK RIE RIL |
DOI | 10.1109/ICRAIS59684.2023.10367114 |
DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
EISBN | 9798350306637 |
EndPage | 249 |
ExternalDocumentID | 10367114 |
Genre | orig-research |
GroupedDBID | 6IE 6IL CBEJK RIE RIL |
ID | FETCH-LOGICAL-i119t-740db0df93f1fd58617c49881cb554573cbecbc826944933e1b6f5b11147887b3 |
IEDL.DBID | RIE |
IngestDate | Wed Jun 26 19:24:47 EDT 2024 |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-i119t-740db0df93f1fd58617c49881cb554573cbecbc826944933e1b6f5b11147887b3 |
ORCID | 0000-0003-2271-3602 0000-0001-7890-2416 |
PageCount | 5 |
ParticipantIDs | ieee_primary_10367114 |
PublicationCentury | 2000 |
PublicationDate | 2023-Nov.-6 |
PublicationDateYYYYMMDD | 2023-11-06 |
PublicationDate_xml | – month: 11 year: 2023 text: 2023-Nov.-6 day: 06 |
PublicationDecade | 2020 |
PublicationTitle | 2023 International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS) |
PublicationTitleAbbrev | ICRAIS |
PublicationYear | 2023 |
Publisher | IEEE |
Publisher_xml | – name: IEEE |
Score | 1.8957636 |
Snippet | The determination of bone age is critical for detecting metabolic and endocrine problems in a child's development. It provides important insights on the rate... |
SourceID | ieee |
SourceType | Publisher |
StartPage | 245 |
SubjectTerms | Bone Age Prediction Bones Computational modeling Deep learning Mean Absolute Error (MAE) Pediatric Pediatrics Predictive models Radiographs Training Transfer learning |
Title | Bone Age Estimation of Pediatrics by Analyzing Hand X-Rays Using Deep Learning Technique |
URI | https://ieeexplore.ieee.org/document/10367114 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3PS8MwFA66g3hSceJvInhtTdq0TY5zbmyCQ-YGvY2meRkidMN1h-2vN0nbiYLgLYSUhHyk7-Xlfd9D6J4JnjGSmdsJS4jHNJeeJJB7FGIVQSI5lzY08DKKB1P2nEZpTVZ3XBgAcMln4Nume8tXi3xtQ2XmhIdxQm3Z6n1OgoqsdYDuat3Mh2F33Bm-RSLmNlgShH4z_kflFGc4-kdo1ExZ5Yt8-OtS-vn2lxrjv9d0jNrfHD38urM-J2gPilOUPi4KwJ054J45uhUrES803hXkWGG5wU6IZGu-woOsUDj1xtlmhV3yAH4CWOJadHWOJ43CaxtN-71Jd-DVtRO8d0pF6SWMKEmUFqGmWkXcOCq5wYXTXBoHIkrC3IAnc26JrEyEIVAZ60iaP5_V009keIZahVnyOcKB0BkRoIgKMhYJA6zWDGSiAuNs6gAuUNtuy2xZyWPMmh25_KP_Ch1adByhL75GrfJzDTfGspfy1iH6BS4Wo3w |
link.rule.ids | 310,311,786,790,795,796,802,27958,55109 |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1dS8MwFA0yQX1SceK3EXxtTdu0TR7n3Oh0GzI36NtompshQjdc97D9epN-TBQE30IgJOSQ3Jube85F6J5yllCS6NcJDYlFFROWIJBaDgTSh1AwJkxoYDAMogl9jv24IqsXXBgAKJLPwDbN4i9fztOVCZXpE-4FoWPKVu9qQ094SdfaQ3eVcuZDrz1q9d58HjATLnE9ux7xo3ZKYTq6h2hYT1pmjHzYq1zY6eaXHuO_V3WEmt8sPfy6tT_HaAeyExQ_zjPArRngjj68JS8RzxXeluRYYrHGhRTJRo_CUZJJHFujZL3ERfoAfgJY4Ep2dYbHtcZrE026nXE7sqrqCda74_DcCimRgkjFPeUo6TPtqqQaGeakQrsQfuilGj6RMkNlpdzzwBGB8oW--4yifii8U9TI9JLPEHa5SggHSaSbUJ9raJWiIELpandTuXCOmmZbpotSIGNa78jFH_23aD8aD_rTfm_4cokODFIFvS-4Qo38cwXX2s7n4qZA9wv_j6bS |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2023+International+Conference+on+Recent+Advances+in+Information+Technology+for+Sustainable+Development+%28ICRAIS%29&rft.atitle=Bone+Age+Estimation+of+Pediatrics+by+Analyzing+Hand+X-Rays+Using+Deep+Learning+Technique&rft.au=Palkar%2C+Anisha&rft.au=Shanbhog%2C+Sharisha&rft.au=Medikonda%2C+Jeevan&rft.date=2023-11-06&rft.pub=IEEE&rft.spage=245&rft.epage=249&rft_id=info:doi/10.1109%2FICRAIS59684.2023.10367114&rft.externalDocID=10367114 |