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Session: Innovative Approaches to Global Nutrition Research (Oral 1)

(OR01-01-23) Computer Vision-Assisted Dietary Assessment Through Mobile Phones in School-Age Children in Ghana: Validity Against Weighed Records and Comparison With 24-Hour Recall

Saturday, July 22, 2023
10:00 AM – 10:12 AM ET
Location:  Constitution B
Disclosure(s):
Aulo Gelli, PhD: No relevant financial relationship(s) with ineligible companies to disclose.
Objectives: To validated FRANI (Food Recognition Assistance and Nudging Insights), a mobile phone application with computer-vision assisted dietary assessment, and multi-pass 24-hour recalls (24HR), against weighed records (WR) in primary school aged children in Ghana.

Methods: Dietary intake was assessed on two non-consecutive days using FRANI, WRs and 24HRs. Equivalence of nutrient intake was tested using mixed effect models adjusted for repeated measures, by comparing ratios (FRANI/WR and 24HR/WR) with equivalence margins at 10%, 15% and 20% error bounds. Agreement between methods was assessed using the concordance correlation coefficient (CCC).

Results: Equivalence for FRANI and WR was determined at the 10% bound for niacin and vitamin A, at 15% bound for energy, protein, riboflavin, and vitamin B6, while intakes of fat, iron, vitamin C and zinc intakes were equivalent at the 20% bound. Comparisons between from 24HR and WR found energy, niacin, riboflavin and zinc intake estimates falling within a 10% bound. Protein, fat, fibre, and calcium intakes were equivalent at the 15% bound; and carbohydrate, folate, niacin, thiamine and vitamin B6 were equivalent at the 20% bound. The CCCs between FRANI and WR ranged between 0.47 and 0.72 (mean=0.59), and between 0.28 and 0.76 (mean=0.5) for 24HR and WR.

Conclusions: FRANI-assisted dietary assessment and 24HRs were found to accurately estimate nutrient intake in primary school aged children in urban Ghana. Though improvements in the computer vision assisted classification and portion estimation are possible, evidence in this emerging research area suggests that this technology is ready for scale-up.

Funding Sources: World Food Programme