Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images

  • Marco Recenti Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
  • Carlo Ricciardi | Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík; Department of Advanced Biomedical Sciences, University Hospital of Naples ‘Federico II’, Naples, Italy.
  • Kyle Edmunds Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
  • Magnus K. Gislason Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
  • Paolo Gargiulo Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík; Department of Science, Landspítali, Reykjavík, Iceland.


The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass indexand isometric leg strength using tree-based regression algorithms. Results obtained from thesemodels demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parametersand comorbidities.



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Focus on 30 Years of Translational Mobility Medicine - Communications
Machine Learning, Soft Tissue, Computed Tomography, Body Mass Index, Isometric Leg Strength
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How to Cite
Recenti, M., Ricciardi, C., Edmunds, K., Gislason, M. K., & Gargiulo, P. (2020). Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images. European Journal of Translational Myology, 30(1), 121-124.

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