Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass

• Published in: European journal of clinical nutrition Vol. 78; no. 5; pp. 452 - 454
• Main Authors: Marazzato, Frederic, McCarthy, Cassidy, Field, Ryan H., Nguyen, Han, Nguyen, Thao, Shepherd, John A., Tinsley, Grant M., Heymsfield, Steven B.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.05.2024; Nature Publishing Group
• Subjects: 13/56; 631/114/2397; 692/308/53; 82/56; 9/10; Absorptiometry, Photon - methods; Adolescent; Adult; Aged; Algorithms; Anthropometry; Anthropometry - methods; Body Composition; Brief Communication; Clinical Nutrition; Datasets; Dual energy X-ray absorptiometry; Epidemiology; Ethnic factors; Female; Humans; Internal Medicine; Machine learning; Male; Mathematical models; Medicine; Medicine & Public Health; Metabolic Diseases; Middle Aged; Minority & ethnic groups; Neural networks; Neural Networks, Computer; Optical scanners; Predictions; Public Health; Statistical analysis; Statistical methods; Statistical models; Young Adult

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 av...