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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.ORCID iD: 0000-0003-0449-5322
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2019 (English)In: PLOS ONE, 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: 2023-12-04Bibliographically approved
In thesis
1. Machine learning applications in healthcare
Open this publication in new window or tab >>Machine learning applications in healthcare
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare.

The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts.

In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment.

In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 202
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
Keywords
Machine learning, Healthcare, Diagnosis, Prognosis, Age assessment, Bone age assessment, Dementia, Applied health technology
National Category
Other Medical Sciences
Research subject
Applied Health Technology
Identifiers
urn:nbn:se:bth-19513 (URN)978-91-7295-405-2 (ISBN)
Public defence
2020-09-16, 14:00
Opponent
Supervisors
Available from: 2020-05-29 Created: 2020-05-28 Last updated: 2021-01-27Bibliographically approved

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Dallora Moraes, Ana LuizaAnderberg, PeterMendes, EmiliaSanmartin Berglund, Johan

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