{"152183f0edd23d3a6b697219f2c6ca41inspo5":{"DOI":"10.14814/phy2.70187","ISBN":"","ISSN":"2051-817X","URL":"http://dx.doi.org/10.14814/phy2.70187","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.","annote":"","author":[{"family":"Ramedani","given":"Saied"},{"family":"Kelesoglu","given":"Ebru"},{"family":"Stutzig","given":"Norman"},{"family":"Von Tengg‐Kobligk","given":"Hendrik"},{"family":"Daneshvar Ghorbani","given":"Keivan"},{"family":"Siebert","given":"Tobias"}],"citation-label":"Ramedani_2025","collection-editor":[{"family":"Siebert","given":"Tobias"}],"collection-title":"","container-author":[{"family":"Siebert","given":"Tobias"}],"container-title":"Physiological Reports","documents":[],"edition":"","editor":[{"family":"Siebert","given":"Tobias"}],"event-date":{"date-parts":[["2025","01"]],"literal":"2025"},"event-place":"","id":"152183f0edd23d3a6b697219f2c6ca41inspo5","interhash":"3afe397a451bce869962d016312aff6a","intrahash":"152183f0edd23d3a6b697219f2c6ca41","issue":"3","issued":{"date-parts":[["2025","01"]],"literal":"2025"},"keyword":"imaging deep sports learning medicine body resonance system composition machine musculoskeletal magnetic","misc":{"issn":"2051-817X","doi":"10.14814/phy2.70187"},"note":"","number":"3","page":"","page-first":"","publisher":"Wiley","publisher-place":"","status":"","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","type":"article-journal","username":"inspo5","version":"","volume":"13"},"1478bcee911cc1bd986507ee4394ed92inspo5":{"DOI":"10.1038/s41598-020-66124-4","ISBN":"","ISSN":"2045-2322","URL":"https://doi.org/10.1038/s41598-020-66124-4","abstract":"The consideration of the temporal and electromyographic (EMG) characteristics of stretch-shortening cycles (SSC) are crucial for the conceptualization of discipline-specific testing and training. Since leg muscles are first stretched (eccentric) and then contracted (concentric) during rowing, it can be assumed that the entire muscle tendon complex performs a SSC. Thus, it should be elucidated whether the rowing cycle can be attributed to either a slow or fast SSC. Therefore, EMG of the vastus medialis and gastrocnemius were captured (nþinspace=þinspace10, 22.8þinspace±þinspace3.1 years, 190þinspace±þinspace6þinspacecm, 82.1þinspace±þinspace9.8þinspacekg) during (single scull) rowing and subsequently compared to typical slow (countermovement jump, CMJ) and fast (drop jump, DJ) SSCs. The elapsed time between the EMG onset and the start of the eccentric phase was monitored. The pre-activation phase (PRE, before the start of the eccentric phase) and the reflex-induced activation phase (RIA 30--120þinspacems after the start of the eccentric phase) have been classified. Notable muscular activity was observed during DJ before the start of the eccentric phase (PRE) as well as during RIA. In contrast, neither CMJ nor rowing revealed any EMG-activity in these two phases. Interestingly, CMJ and race-specific rowing showed an EMG-onset during the eccentric phase. We conclude that rowing is more attributable to a slow SSC and implies that fast SSC does not reflect discipline specific muscle action and could hamper rowing-performance-enhancement.","annote":"","author":[{"family":"Held","given":"Steffen"},{"family":"Siebert","given":"Tobias"},{"family":"Donath","given":"Lars"}],"citation-label":"Held2020","collection-editor":[{"family":"Siebert","given":"Tobias"}],"collection-title":"","container-author":[{"family":"Siebert","given":"Tobias"}],"container-title":"Scientific Reports","documents":[],"edition":"","editor":[{"family":"Siebert","given":"Tobias"}],"event-date":{"date-parts":[["2020","06","11"]],"literal":"2020"},"event-place":"","id":"1478bcee911cc1bd986507ee4394ed92inspo5","interhash":"5ab9f68e8c6f6627e1fba64e5170fc51","intrahash":"1478bcee911cc1bd986507ee4394ed92","issue":"1","issued":{"date-parts":[["2020","06","11"]],"literal":"2020"},"keyword":"system Physiology Musculoskeletal","misc":{"issn":"2045-2322","doi":"10.1038/s41598-020-66124-4"},"note":"","number":"1","page":"9451","page-first":"9451","publisher":"","publisher-place":"","status":"","title":"Electromyographic activity of the vastus medialis and gastrocnemius implicates a slow stretch-shortening cycle during rowing in the field","type":"article-journal","username":"inspo5","version":"","volume":"10"},"3cb83562fd4a436e376d28712fe58a37mhartmann":{"DOI":"10.1007/978-3-642-23860-4_52","ISBN":"978-3-642-23859-8","ISSN":"","URL":"http://dx.doi.org/10.1007/978-3-642-23860-4_52","abstract":"","annote":"","author":[{"family":"Hoher","given":"S."},{"family":"Schindler","given":"P."},{"family":"G?ttlich","given":"S."},{"family":"Schleper","given":"V."},{"family":"Röck","given":"S."}],"citation-label":"hoher2012system","collection-editor":[{"family":"ElMaraghy","given":"Hoda A."}],"collection-title":"","container-author":[{"family":"ElMaraghy","given":"Hoda A."}],"container-title":"Enabling Manufacturing Competitiveness and Economic Sustainability","documents":[],"edition":"","editor":[{"family":"ElMaraghy","given":"Hoda A."}],"event-date":{"date-parts":[["2012"]],"literal":"2012"},"event-place":"","id":"3cb83562fd4a436e376d28712fe58a37mhartmann","interhash":"f49a945ea15206c6cd60d4db77a8d5a5","intrahash":"3cb83562fd4a436e376d28712fe58a37","issue":"","issued":{"date-parts":[["2012"]],"literal":"2012"},"keyword":"simulation; Real-time models system; Material dynamic vorlaeufig flow System","misc":{"isbn":"978-3-642-23859-8","owner":"schleper","language":"English","doi":"10.1007/978-3-642-23860-4_52"},"note":"","number":"","number-of-pages":"5","page":"316-321","page-first":"316","publisher":"Springer Berlin Heidelberg","publisher-place":"","status":"","title":"System Dynamic Models and Real-time Simulation of Complex Material\n\tFlow Systems","type":"chapter","username":"mhartmann","version":"","volume":""},"3824a9778eb687fae1a89272266bd129hermann":{"DOI":"10.1109/CCGrid.2013.42","ISBN":"","ISSN":"","URL":"","abstract":"","annote":"","author":[{"family":"Copil","given":"G."},{"family":"Moldovan","given":"D."},{"family":"Truong","given":"H. L."},{"family":"Dustdar","given":"S."}],"citation-label":"copil2013extensible","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2013","05"]],"literal":"2013"},"event-place":"","id":"3824a9778eb687fae1a89272266bd129hermann","interhash":"7f08b6efcfcf08ea26872619b6fd8f67","intrahash":"3824a9778eb687fae1a89272266bd129","issue":"","issued":{"date-parts":[["2013","05"]],"literal":"2013"},"keyword":"cloud code system SYBL specification Elasticity API soc2018 application program","misc":{"doi":"10.1109/CCGrid.2013.42"},"note":"","number":"","number-of-pages":"7","page":"112-119","page-first":"112","publisher":"","publisher-place":"","status":"","title":"SYBL: An Extensible Language for Controlling Elasticity in Cloud Applications","type":"paper-conference","username":"hermann","version":"","volume":""},"9013ac66f090cc5a7f66979e9af871dehermann":{"DOI":"10.1109/MCC.2016.124","ISBN":"","ISSN":"2325-6095","URL":"","abstract":"","annote":"","author":[{"family":"Villari","given":"M."},{"family":"Fazio","given":"M."},{"family":"Dustdar","given":"S."},{"family":"Rana","given":"O."},{"family":"Ranjan","given":"R."}],"citation-label":"villari2016osmotic","collection-editor":[],"collection-title":"","container-author":[],"container-title":"IEEE Cloud Computing","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2016","11"]],"literal":"2016"},"event-place":"","id":"9013ac66f090cc5a7f66979e9af871dehermann","interhash":"0f7f2e87010c43073ec2dd54a7d05fad","intrahash":"9013ac66f090cc5a7f66979e9af871de","issue":"6","issued":{"date-parts":[["2016","11"]],"literal":"2016"},"keyword":"cloud system transfer computing edge-cloud distributed soc2018 IoT","misc":{"issn":"2325-6095","doi":"10.1109/MCC.2016.124"},"note":"","number":"6","number-of-pages":"7","page":"76-83","page-first":"76","publisher":"","publisher-place":"","status":"","title":"Osmotic Computing: A New Paradigm for Edge/Cloud Integration","type":"article-journal","username":"hermann","version":"","volume":"3"}}