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

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).

Materials and methods

Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment

SUBMITTER: Shonkoff E 

PROVIDER: S-EPMC10836267 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature