RESEARCH ARTICLE Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis Ana Luiza Dallora ID 1 *, Peter Anderberg ID 1‡ , Ola Kvist ID 2☯ , Emilia Mendes 3‡ , Sandra Diaz Ruiz 2☯ , Johan Sanmartin Berglund 1‡ 1 Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden, 2 Department of Pediatric Radiology, Karolinska University Hospital, Stockholm, Sweden, 3 Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work * ana.luiza.moraes@bth.se 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 individ- ual 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, fol- lowed 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 PLOS ONE | https://doi.org/10.1371/journal.pone.0220242 July 25, 2019 1 / 22 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Dallora AL, Anderberg P, Kvist O, Mendes E, Diaz Ruiz S, Sanmartin Berglund J (2019) Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis. PLoS ONE 14(7): e0220242. https:// doi.org/10.1371/journal.pone.0220242 Editor: Ruxandra Stoean, University of Craiova, ROMANIA Received: April 2, 2019 Accepted: July 11, 2019 Published: July 25, 2019 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. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist.