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Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

ABSTRACT: 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.

SUBMITTER: Dallora AL

PROVIDER: S-EPMC6657881 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

Dallora Ana Luiza AL Anderberg Peter P Kvist Ola O Mendes Emilia E Diaz Ruiz Sandra S Sanmartin Berglund Johan J

PloS one 20190725 7

<h4>Background</h4>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 Learni ...[more]

PMID: 31344143

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Dataset Information

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

Publications

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

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