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

Marco Recenti, Carlo Ricciardi, Kyle Edmunds, Magnus K Gislason, Paolo Gargiulo

Research output: Contribution to journalArticlepeer-review

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. Keywords: Computed Tomography; Machine learning; body mass index; isometric leg strength; soft tissue.
Original languageEnglish
Pages (from-to)8892
JournalEuropean Journal of Translational Myology
Volume30
Issue number1
DOIs
Publication statusPublished - 1 Apr 2020

Other keywords

  • Computed Tomography
  • Machine learning
  • body mass index
  • isometric leg strength
  • soft tissue

Fingerprint

Dive into the research topics of 'Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.'. Together they form a unique fingerprint.

Cite this