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

SUBMITTER: Dallora AL 

PROVIDER: S-EPMC6657881 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature