Towards the Development of a Comprehensive Tissue Mass Prediction Tool for Living Men and Women

Accurate tissue mass estimates from living people are required to better model the response of the musculoskeletal system following impulsive impact events [1]. To date, prediction equations have been developed to estimate the soft and rigid tissue masses of all body segments from surface anthropometric measures and validated against actual tissue masses obtained from Dual-energy X-ray Absorptiometry scans (DXA) [2-5]. Good to excellent reliability has been reported for all anthropometric measures (lengths, circumferences, breadths, skinfolds) [6-7], and tissue masses obtained manually from the DXA scans [8-9]. Despite the accuracy and reliability of these equations, this process of obtaining tissue masses from living people is limited by: inconsistencies in variable names and measurement definitions between studies; susceptibility of errors when transferring written measurements to digital form, and; substantial time spent on an overly complex manual process. Therefore, the intention of the current study is to address the limitations of the current process used to estimate segment-specific soft and rigid tissue masses by developing a tool that incorporates existing equations into one location (with unified variable names and measurement definitions), automating, and linking the data recording and analysis processes. Existing tissue mass prediction equations will be compiled on a tablet device for the segments of the upper and lower extremities, as well as the head, neck, trunk and pelvis. A simple user-interface is being developed using MATLABâ(R2017a) to enable input of anthropometric measurements using an interactive human manikin. Measurement inputs will be linked to the prediction equations to streamline the analysis process. Additionally, variable names and measurement definitions from the different equations have been reconciled to increase usability. This tool will facilitate the prediction of soft and rigid tissue masses from anthropometric measurements by increasing data collection efficiency, reducing data transfer errors, and enabling large population studies of tissue masses for all body segments

MANUAL SEGMENTATION OF HEAD, NECK, TRUNK AND PELVIS SEGMENTS FROM DXA SCANS IS A RELIABLE APPROACH FOR DETERMINING TISSUE MASS ESTIMATES

To facilitate the use of wobbling mass models in biomechanical research, accurate and reliable quantification of in-vivo soft and rigid tissue masses is required [1]. The reliability of upper and lower extremity tissue mass estimates has been reported to be good to excellent, following manual segmentation of Dual Energy X-ray Absorptiometry (DXA) scans [2]. The purpose of this study was to quantify the within- and between-measurer reliability of the head, neck, trunk, and pelvis tissue masses, using a comparable approach. Full body DXA scans were performed on 102 younger (51F, 51M; 16-35 years) and 101 older (50F, 51M; 36-65 years) participants, and manually segmented twice by four trained measurers using regions of interest for the head, neck, trunk and pelvis segments. Between- and within-measurer reliability of segment lean mass (LM), fat mass (FM), wobbling mass (WM=LM+FM) and bone mineral content (BMC) were assessed using intra-class correlation coefficients (ICCs) (good to excellent: ICCs\u3e0.75) [3] and coefficients of variation (CVs) (good: CVs Within- and between-measurer ICCs ranged from 0. 595 (neck BMC) to 1.00 (trunk WM, FM, BMC; head BMC; pelvis BMC), and 0.523 (neck LM) to 1.000 (head BMC; trunk WM, FM), respectively. Over 95% of all CV values had magnitudes below 5%, and maximum between- and within-measurer CVs were only 7.34% (neck BMC) and 6.28% (neck FM). All tissue mass estimates (across segments and age groups) had good to excellent reliability except a few ICCs for the neck and head. Limitations with planar scanning of the neck and head contributed to this finding. Overall, these very positive results are consistent with previous work for the extremities [2] and suggest that manual segmentation of core body segments from DXA scans is an appropriate approach for determining tissue mass estimates of living people of a range of ages