Artificial intelligence and body composition.

Journal: Diabetes & metabolic syndrome
PMID: 36867973

Abstract

AIMS: 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.

Authors

  • Prasanna Santhanam
    Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Tanmay Nath
    Department of Biostatistics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
  • Cheng Peng
    School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei, China.
  • Harrison Bai
    Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
  • Helen Zhang
    The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
  • Rexford S Ahima
    Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Rama Chellappa

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