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Journal of Medical Systems

, 38:87 | Cite as

Impact of Ensemble Learning in the Assessment of Skeletal Maturity

  • Pedro Cunha
  • Daniel C. Moura
  • Miguel Angel Guevara López
  • Conceição Guerra
  • Daniela Pinto
  • Isabel Ramos
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children’s bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

Keywords

Bone age Ensembles Regression Bagging Digital imaging Machine learning 

Notes

Acknowledgments

This work was partially supported by the Cloud Thinking project (CENTRO-07-ST24-FEDER-002031), co-funded by “Quadro de Referência Estratégica Nacional “(QREN), “Mais Centro” program.

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

10916_2014_87_MOESM1_ESM.csv (2 mb)
ESM 1 (csv 1.96 MB)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Pedro Cunha
    • 1
  • Daniel C. Moura
    • 2
  • Miguel Angel Guevara López
    • 3
  • Conceição Guerra
    • 4
  • Daniela Pinto
    • 4
  • Isabel Ramos
    • 5
  1. 1.Laboratory of Experimental Mechanics and Optics (LOME) Institute of Mechanical Engineering and Industrial Management (INEGI) Campus da FEUPPortoPortugal
  2. 2.Instituto de Telecomunicações, Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.IEETA – Department of Electronics, Telecommunications and Informatics, University of Aveiro (UA)Campus Universitário de SantiagoAveiroPortugal
  4. 4.Centro Hospitalar São João, Porto, Portugal Alameda Prof. Hernâni MonteiroPortoPortugal
  5. 5.FMUP-HSJ – Faculty of Medicine – Centro Hospitalar São JoãoUniversity of Porto, Portugal Alameda Prof. Hernâni MonteiroPortoPortugal

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