Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0002-6752-017X
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0001-9870-8477
KI, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Show others and affiliations
2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed) Published
Abstract [en]

Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Place, publisher, year, edition, pages
Public Library of Science , 2019. Vol. 14, no 7, article id e0220242
National Category
Other Medical Sciences not elsewhere specified
Identifiers
URN: urn:nbn:se:bth-18620DOI: 10.1371/journal.pone.0220242ISI: 000484977900073Scopus ID: 2-s2.0-85069805545OAI: oai:DiVA.org:bth-18620DiVA, id: diva2:1349902
Note

open access

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-10-09Bibliographically approved

Open Access in DiVA

fulltext(1153 kB)72 downloads
File information
File name FULLTEXT01.pdfFile size 1153 kBChecksum SHA-512
1c3f6c62f9b4119704fee43e04134c6a5c6c73da62155c6f8b2c6e80194ec304aad4baa8380ddb23ae773751e2b2c76eba716f47add4fe3eb67c68b95ac8073e
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

Moraes, Ana Luiza DalloraAnderberg, PeterMendes, EmiliaSanmartin Berglund, Johan

Search in DiVA

By author/editor
Moraes, Ana Luiza DalloraAnderberg, PeterMendes, EmiliaSanmartin Berglund, Johan
By organisation
Department of HealthDepartment of Software Engineering
In the same journal
PLoS ONE
Other Medical Sciences not elsewhere specified

Search outside of DiVA

GoogleGoogle Scholar
Total: 72 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 238 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf