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Semin Musculoskelet Radiol 2020; 24(01): 030-037
DOI: 10.1055/s-0039-3400267
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Artificial Intelligence in the Evaluation of Body Composition

Benjamin Wang
1   Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Martin Torriani
1   Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
› Author Affiliations Funding Source Martin Torriani was funded in part by the National Institutes of Health Nutrition and Obesity Research Center at Harvard University (P30 DK040561).
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Publication History

Publication Date:
28 January 2020 (online)

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Abstract

Body composition entails the measurement of muscle and fat mass in the body and has been shown to impact clinical outcomes in various aspects of human health. As a result, the need is growing for reliable and efficient noninvasive tools to measure body composition. Traditional methods of estimating body composition, anthropomorphic measurements, dual-energy X-ray absorptiometry, and bioelectrical impedance, are limited in their application. Cross-sectional imaging remains the reference standard for body composition analysis and is accomplished through segmentation of computed tomography and magnetic resonance imaging studies. However, manual segmentation of images by an expert reader is labor intensive and time consuming, limiting its implementation in large-scale studies and in routine clinical practice. In this review, novel methods to automate the process of body composition measurement are discussed including the application of artificial intelligence and deep learning to tissue segmentation.

Keywords

body composition analysis - metabolic imaging - artificial intelligence - machine learning - neural networks
 

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