https://doi.org/10.4081/ejtm.2019.8892
Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images
Submitted: 11 February 2020
Accepted: 3 March 2020
Published: 1 April 2020
Accepted: 3 March 2020
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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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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. https://doi.org/10.4081/ejtm.2019.8892
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