Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.
Frederic MarazzatoCassidy McCarthyRyan H FieldHan NguyenThao NguyenJohn A ShepherdGrant M TinsleySteven B HeymsfieldPublished in: European journal of clinical nutrition (2023)
Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machine learning approaches are increasingly publicly available and have key advantages over statistical modeling methods when developing prediction algorithms on large datasets with multiple complex covariates. This study aimed to test the feasibility of predicting DXA-measured appendicular lean mass (ALM) with a neural network (NN) algorithm developed on a sample of 576 participants using 10 demographic (sex, age, 7 ethnic groupings) and 43 anthropometric dimensions generated with a 3D optical scanner. NN-predicted and measured ALM were highly correlated (n = 116; R 2 , 0.95, p < 0.001, non-significant bias) with small mean, absolute, and root-mean square errors (X ± SD, -0.17 ± 1.64 kg and 1.28 ± 1.04 kg; 1.64). These observations demonstrate the application of NN body composition prediction algorithms to rapidly emerging large and complex digital anthropometric datasets. Clinical Trial Registration: NCT03637855, NCT05217524, NCT03771417, and NCT03706612.
Keyphrases
- body composition
- neural network
- machine learning
- bone mineral density
- resistance training
- clinical trial
- deep learning
- artificial intelligence
- big data
- magnetic resonance imaging
- high resolution
- rna seq
- patient safety
- single cell
- magnetic resonance
- study protocol
- phase ii
- adverse drug
- drug induced
- phase iii
- contrast enhanced
- postmenopausal women