%0 Journal Article %1 Ramedani_2025 %A Ramedani, Saied %A Kelesoglu, Ebru %A Stutzig, Norman %A Von Tengg‐Kobligk, Hendrik %A Daneshvar Ghorbani, Keivan %A Siebert, Tobias %D 2025 %E Siebert, Tobias %I Wiley %J Physiological Reports %K PN2 selected %N 3 %R 10.14814/phy2.70187 %T Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females %U http://dx.doi.org/10.14814/phy2.70187 %V 13 %X AbstractThe maintenance of an appropriate ratio of body fat to muscle mass is essentialfor the preservation of health and performance, as excessive body fat is associatedwith an increased risk of various diseases. Accurate body composition assessmentrequires precise segmentation of structures. In this study we developed a novelautomatic machine learning approach for volumetric segmentation and quantita-tive assessment of MRI volumes and investigated the efficacy of using a machinelearning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bonevolume 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 endur-ance training plan that was conducted two times per week. Before and after thetraining, the subjects of both groups underwent MRI scanning. The evaluation ofthe image data was performed by a machine learning system which is based ona 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 repeatedmeasures for the factor TIME. The results of the ANOVA demonstrate significantTIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003,ηP2 = 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 volumebetween the groups. This study supports the use of artificial intelligence systemsto analyze MRI images as a reliable tool for monitoring training responses onbody composition.

@article{Ramedani_2025, abstract = {AbstractThe maintenance of an appropriate ratio of body fat to muscle mass is essentialfor the preservation of health and performance, as excessive body fat is associatedwith an increased risk of various diseases. Accurate body composition assessmentrequires precise segmentation of structures. In this study we developed a novelautomatic machine learning approach for volumetric segmentation and quantita-tive assessment of MRI volumes and investigated the efficacy of using a machinelearning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bonevolume 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 endur-ance training plan that was conducted two times per week. Before and after thetraining, the subjects of both groups underwent MRI scanning. The evaluation ofthe image data was performed by a machine learning system which is based ona 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 repeatedmeasures for the factor TIME. The results of the ANOVA demonstrate significantTIME × GROUP interaction effects for the muscle volume (F1,16 = 12.80, p = 0.003,ηP2 = 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 volumebetween the groups. This study supports the use of artificial intelligence systemsto analyze MRI images as a reliable tool for monitoring training responses onbody composition.}, added-at = {2025-02-28T16:06:55.000+0100}, author = {Ramedani, Saied and Kelesoglu, Ebru and Stutzig, Norman and Von Tengg‐Kobligk, Hendrik and Daneshvar Ghorbani, Keivan and Siebert, Tobias}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2152183f0edd23d3a6b697219f2c6ca41/exc2075}, doi = {10.14814/phy2.70187}, editor = {Siebert, Tobias}, interhash = {3afe397a451bce869962d016312aff6a}, intrahash = {152183f0edd23d3a6b697219f2c6ca41}, issn = {2051-817X}, journal = {Physiological Reports}, keywords = {PN2 selected}, month = {01}, number = 3, publisher = {Wiley}, timestamp = {2025-02-28T16:06:55.000+0100}, title = {Quantification of training‐induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females}, url = {http://dx.doi.org/10.14814/phy2.70187}, volume = 13, year = 2025 }