AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.

Journal: Annals of medicine
PMID: 38060823

Abstract

OBJECTIVE: Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water).

Authors

  • Eleanor Shonkoff
    School of Health Sciences, Merrimack College, North Andover, MA, USA.
  • Kelly Copeland Cara
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Xuechen Anna Pei
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Mei Chung
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Shreyas Kamath
    School of Engineering, Tufts University, Medford, MA, USA.
  • Karen Panetta
    School of Engineering, Tufts University, Medford, MA, USA.
  • Erin Hennessy
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.

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