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Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa.ORCID-id: 0000-0002-6752-017X
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa.ORCID-id: 0000-0001-9870-8477
KI, SWE.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik.ORCID-id: 0000-0003-0449-5322
Vise andre og tillknytning
2019 (engelsk)Inngår i: PLOS ONE, E-ISSN 1932-6203, Vol. 14, nr 7, artikkel-id e0220242Artikkel, forskningsoversikt (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Public Library of Science , 2019. Vol. 14, nr 7, artikkel-id e0220242
HSV kategori
Identifikatorer
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
Merknad

open access

Tilgjengelig fra: 2019-09-10 Laget: 2019-09-10 Sist oppdatert: 2023-12-04bibliografisk kontrollert
Inngår i avhandling
1. Machine learning applications in healthcare
Åpne denne publikasjonen i ny fane eller vindu >>Machine learning applications in healthcare
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2020. s. 202
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
Emneord
Machine learning, Healthcare, Diagnosis, Prognosis, Age assessment, Bone age assessment, Dementia, Applied health technology
HSV kategori
Forskningsprogram
Tillämpad hälsoteknik
Identifikatorer
urn:nbn:se:bth-19513 (URN)978-91-7295-405-2 (ISBN)
Disputas
2020-09-16, 14:00
Opponent
Veileder
Tilgjengelig fra: 2020-05-29 Laget: 2020-05-28 Sist oppdatert: 2021-01-27bibliografisk kontrollert

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

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