Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults

PurposeTo train and test a machine learning model to automatically measure mid-thigh muscle cross-sectional area (CSA) to provide rapid estimation of appendicular lean mass (ALM) and predict knee extensor torque of obese adults.MethodsObese adults [body mass index (BMI) = 30-40 kg/m2, age = 30-50 ye...

Full description

Saved in:
Bibliographic Details
Main Authors: Stephan G. Bodkin (Author), Andrew C. Smith (Author), Bryan C. Bergman (Author), Donglai Huo (Author), Kenneth A. Weber (Author), Simona Zarini (Author), Darcy Kahn (Author), Amanda Garfield (Author), Emily Macias (Author), Michael O. Harris-Love (Author)
Format: Book
Published: Frontiers Media S.A., 2022-03-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_f7f91e54f7c9470cb5c4fe2f0fbfeff3
042 |a dc 
100 1 0 |a Stephan G. Bodkin  |e author 
700 1 0 |a Stephan G. Bodkin  |e author 
700 1 0 |a Andrew C. Smith  |e author 
700 1 0 |a Bryan C. Bergman  |e author 
700 1 0 |a Donglai Huo  |e author 
700 1 0 |a Kenneth A. Weber  |e author 
700 1 0 |a Simona Zarini  |e author 
700 1 0 |a Darcy Kahn  |e author 
700 1 0 |a Amanda Garfield  |e author 
700 1 0 |a Emily Macias  |e author 
700 1 0 |a Michael O. Harris-Love  |e author 
245 0 0 |a Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults 
260 |b Frontiers Media S.A.,   |c 2022-03-01T00:00:00Z. 
500 |a 2673-6861 
500 |a 10.3389/fresc.2022.808538 
520 |a PurposeTo train and test a machine learning model to automatically measure mid-thigh muscle cross-sectional area (CSA) to provide rapid estimation of appendicular lean mass (ALM) and predict knee extensor torque of obese adults.MethodsObese adults [body mass index (BMI) = 30-40 kg/m2, age = 30-50 years] were enrolled for this study. Participants received full-body dual-energy X-ray absorptiometry (DXA), mid-thigh MRI, and completed knee extensor and flexor torque assessments via isokinetic dynamometer. Manual segmentation of mid-thigh CSA was completed for all MRI scans. A convolutional neural network (CNN) was created based on the manual segmentation to develop automated quantification of mid-thigh CSA. Relationships were established between the automated CNN values to the manual CSA segmentation, ALM via DXA, knee extensor, and flexor torque.ResultsA total of 47 obese patients were enrolled in this study. Agreement between the CNN-automated measures and manual segmentation of mid-thigh CSA was high (>0.90). Automated measures of mid-thigh CSA were strongly related to the leg lean mass (r = 0.86, p < 0.001) and ALM (r = 0.87, p < 0.001). Additionally, mid-thigh CSA was strongly related to knee extensor strength (r = 0.76, p < 0.001) and moderately related to knee flexor strength (r = 0.48, p = 0.002).ConclusionCNN-measured mid-thigh CSA was accurate compared to the manual segmented values from the mid-thigh. These values were strongly predictive of clinical measures of ALM and knee extensor torque. Mid-thigh MRI may be utilized to accurately estimate clinical measures of lean mass and function in obese adults. 
546 |a EN 
690 |a obesity 
690 |a MRI 
690 |a quadriceps 
690 |a intramuscular adipose tissue 
690 |a convolutional neural network (CNN) 
690 |a Other systems of medicine 
690 |a RZ201-999 
690 |a Medical technology 
690 |a R855-855.5 
655 7 |a article  |2 local 
786 0 |n Frontiers in Rehabilitation Sciences, Vol 3 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fresc.2022.808538/full 
787 0 |n https://doaj.org/toc/2673-6861 
856 4 1 |u https://doaj.org/article/f7f91e54f7c9470cb5c4fe2f0fbfeff3  |z Connect to this object online.