Ramedani Saied, Kelesoglu Ebru, Stutzig Norman, Von Tengg-Kobligk Hendrik, Daneshvar Ghorbani Keivan, Siebert Tobias
Graduate School of Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.
Department of Diagnostic, Interventional and Pediatric Radiology, Bern University Hospital, University of Bern, Bern, Switzerland.
Physiol Rep. 2025 Feb;13(3):e70187. doi: 10.14814/phy2.70187.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training. Eighteen healthy, young, female volunteers were randomly allocated to two groups: intervention group (IG) and control group (CG). The IG group followed an 8-week strength endurance training plan that was conducted two times per week. Before and after the training, the subjects of both groups underwent MRI scanning. The evaluation of the image data was performed by a machine learning system which is based on a 3D U-Net-based Convolutional Neural Network. The volumes of muscle, bone, and SAT were each examined using a 2 (GROUP [IG vs. CG]) × 2 (TIME [pre-intervention vs. post-intervention]) analysis of variance (ANOVA) with repeated measures for the factor TIME. The results of the ANOVA demonstrate significant TIME × GROUP interaction effects for the muscle volume (F = 12.80, p = 0.003, η = 0.44) with an increase of 2.93% in the IG group and no change in the CG (-0.62%, p = 0.893). There were no significant changes in bone or SAT volume between the groups. This study supports the use of artificial intelligence systems to analyze MRI images as a reliable tool for monitoring training responses on body composition.
保持适当的体脂与肌肉量比例对于维持健康和身体机能至关重要，因为过多的体脂会增加患各种疾病的风险。准确的身体成分评估需要对结构进行精确分割。在本研究中，我们开发了一种新颖的自动机器学习方法，用于对MRI容积进行体积分割和定量评估，并研究了使用机器学习算法评估力量训练前后大腿肌肉、皮下脂肪组织（SAT）和骨体积的效果。18名健康、年轻的女性志愿者被随机分为两组：干预组（IG）和对照组（CG）。IG组遵循一项为期8周的力量耐力训练计划，每周进行两次。训练前后，两组受试者均接受了MRI扫描。图像数据的评估由一个基于3D U-Net卷积神经网络的机器学习系统进行。使用2（组[IG与CG]）×2（时间[干预前与干预后]）方差分析（ANOVA）对肌肉、骨骼和SAT的体积进行检验，并对时间因素进行重复测量。ANOVA结果显示，肌肉体积存在显著的时间×组交互作用（F = 12.80，p = 0.003，η = 0.44），IG组增加了2.93%，而CG组没有变化（-0.62%，p = 0.893）。两组之间的骨骼或SAT体积没有显著变化。本研究支持使用人工智能系统分析MRI图像，作为监测身体成分训练反应的可靠工具。
