ArticleEuropean journal of translational myology2020
Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.
Article in European journal of translational myology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 40 citations in OpenAlex.
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- Trauma of Peripheral Innervation Impairs Content of Epidermal Langerhans Cells.Diagnostics (Basel, Switzerland) · 2022Article
- Length-dependent changes of lower limb muscle morphology in Chronic Inflammatory Demyelinating Polyneuropathy assessed with magnetic resonance imaging.European journal of translational myology · 2021Article
- Muscle Oxygenation Level Might Trigger the Regulation of Capillary Venous Blood Filling during Fatiguing Isometric Muscle Actions.Diagnostics (Basel, Switzerland) · 2021Article
- Testing soft tissue radiodensity parameters interplay with age and self-reported physical activity.European journal of translational myology · 2021Article
- Skeletal muscle weakness in older adults home-restricted due to COVID-19 pandemic: a role for full-body in-bed gym and functional electrical stimulation.Aging clinical and experimental research · 2021Article
- To contrast and reverse skeletal muscle weakness by Full-Body In-Bed Gym in chronic COVID-19 pandemic syndrome.European journal of translational myology · 2021Article
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- Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.Computational and mathematical methods in medicine · 2021Article
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.