Chris Stewart, Evert O. Wesselink, Zuzana Perraton, Kenneth A. Weber, Matthew G. King, Joanne L. Kemp, Benjamin F. Mentiplay, Kay M. Crossley, James M. Elliott, Joshua J. Heerey, Mark J. Scholes, Peter R. Lawrenson, Chris Calabrese, Adam I. Semciw
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Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach
Hip-related pain (HRP) affects young to middle-aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep-learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine-learning segmentations compared with human-generated segmentation.
期刊介绍:
The Journal of Cachexia, Sarcopenia, and Muscle is a prestigious, peer-reviewed international publication committed to disseminating research and clinical insights pertaining to cachexia, sarcopenia, body composition, and the physiological and pathophysiological alterations occurring throughout the lifespan and in various illnesses across the spectrum of life sciences. This journal serves as a valuable resource for physicians, biochemists, biologists, dieticians, pharmacologists, and students alike.