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

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


Title: Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images
Author: Recenti, Marco   orcid.org/0000-0001-9440-8434
Ricciardi, Carlo   orcid.org/0000-0001-7290-6432
Edmunds, Kyle   orcid.org/0000-0002-6591-4116
Gislason, Magnus K.
Gargiulo, Paolo   orcid.org/0000-0002-5049-4817
Date: 2020-04-01
Language: English
Scope: 121-124
University/Institute: Háskólinn í Reykjavík
Reykjavik University
School: Tæknisvið (HR)
School of Technology (RU)
Department: Institute of Biomedical and Neural Engineering (IBNE)
Series: European Journal of Translational Myology;30(1)
ISSN: 2037-7452
2037-7460 (eISSN)
DOI: 10.4081/ejtm.2019.8892
Subject: Machine learning; Soft tissue; Computed tomography; Body mass index; Isometric leg strength; Vélrænt nám; Stoðvefur; Sneiðmyndatökur; Líkamsþyngdarstuðull; Stoðkerfi (líffærafræði)
URI: https://hdl.handle.net/20.500.11815/2451

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Citation:

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

Abstract:

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 index and isometric leg strength using tree-based regression algorithms. Results obtained from these models 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 parameters and comorbidities.

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This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (CC BY-NC 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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