Predicting body mass index and isometric leg strength using soft tissue distributions from computed tomography scans
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ORIGINAL PAPER
Predicting body mass index and isometric leg strength using soft tissue distributions from computed tomography scans Marco Recenti 1 & Carlo Ricciardi 1,2 & Anaïs Monet 1 & Deborah Jacob 1 & Jorgelina Ramos 1 & Magnus Gìslason 1 & Kyle Edmunds 1 & Ugo Carraro 3 & Paolo Gargiulo 1,4 Received: 23 December 2019 / Accepted: 22 October 2020 # IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper describes the interconnections and predictive value between Body Mass Index (BMI), Isometric Leg Strength (ISO) and soft tissue distribution from mid-thigh Computed Tomography (CT) scans using Machine Learning (ML) regression and classification algorithms. A novel methodology for soft tissue patient specific CT profile called Nonlinear Trimodal Regression Analysis (NTRA) was developed using radiodensitomentric distribution from a CT scan. This method defines 11 parameters used as input features for Tree-Based ML algorithms in order to apply regression and classification on BMI and ISO. K_fold Cross-Validation with k = 10 is applied to obtain several models to choose the best one using the higher coefficient of determination (R2) as an evaluator of the quality of regression prediction. Following this, BMI and ISO are divided into 3 and 5 classes and the same methodology is used to classify them. For this analysis, an accuracy parameter is calculated to evaluate the quality of the results. The max R2 is 88.9 for the BMI and it is obtained using the Gradient-Boosting Algorithm. The best accuracy was 76.1 for 3 classes and 73.1 for 5 classes. The best results obtained for ISO are R2 = 66.5 and an accuracy of 65.5 for the 3 classes classification. Furthermore, the connective tissue assumes high importance in the prediction process. In this methodological study the feasibility of a ML approach was tested with good results, in order to show a novel approach to study the correlation between physiology parameters and imaging. Keywords Machine learning . Soft tissue . Computed tomography . Body mass index . Isometric leg strength
1 Introduction Muscle changes and degeneration, characterized by the loss of strength, function, and mass and substitution of healthy muscles with increased content in fat and fibrous collagen, have been consistently implicated as an independent mortality risk in aging individuals. This phenomenon is defined as
* Marco Recenti [email protected] 1
Institute of Biomedical and Neural Engineering, Reykjavik University, Menntavegi 1, 102 Reykjavik, Iceland
2
Department of Advanced Biomedical Sciences, University Hospital of Naples ‘Federico II’, Via Sergio Pansini 5, 80131 Naples, Italy
3
CIR-Myo, Department of Biomedical Sciences, University of Padova, Via Ugo Bassi 58/B, 35121 Padova, Italy
4
Department of Science, Landspitali, Hingbraut, 101 Reykjavik, Iceland
sarcopenia, in aging individuals, and its prevalence has been observed to incur declines in quality of life and physical activity [1–4]. Artificial Intelligence (AI) technologies, particularly Machine
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