Artificial intelligence and body composition

• Published in: Diabetes & metabolic syndrome clinical research & reviews 2023-03, Vol.17 (3), p.102732-102732, Article 102732
• Main Authors: Santhanam, Prasanna, Nath, Tanmay, Peng, Cheng, Bai, Harrison, Zhang, Helen, Ahima, Rexford S., Chellappa, Rama
• Format: Article
• Language: English
• Subjects: AI and Body composition; Artificial Intelligence; Body Composition; Electrocardiography; Heart Disease Risk Factors; Humans; Hypertension
• ISSN: 1871-4021; 1878-0334
• DOI: 10.1016/j.dsx.2023.102732

Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends. We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review. AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis. AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context. •It is a systematic review designed to evaluate the different AI methods for assessment of body composition.•Artificial Intelligence for body composition measurements enhances our ability to quantify obesity and metabolic risk.•Automated segmentation of body composition is the future of body fat assessment technologies.•There are inherent biases in the application of AI for body composition that needs substantial improvement.