Computer vision-assisted dietary assessment through mobile phones in female youth in urban Ghana: validity against weighed records and comparison with 24-h recalls.

Background: Gaps persist in the data on diets and on the validity of dietary assessment methods in youth in low-income and middle-income countries (LMICs) due to costs constraints. Although computer vision-assisted dietary assessment tools have been proposed, limited evidence exists on their validity in LMICs.

Objectives: This study aimed to validate FRANI (Food Recognition Assistance and Nudging Insights), a mobile phone application with computer vision-assisted dietary assessment, against weighed records (WRs) and compare with 24-h recalls (24HR), in female youth in Ghana.

Validation of Mobile Artificial Intelligence Technology-Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana.

Background: Important gaps exist in the dietary intake of adolescents in low- and middle-income countries (LMICs), partly due to expensive assessment methods and inaccuracy in portion-size estimation. Dietary assessment tools leveraging mobile technologies exist but only a few have been validated in LMICs.

Objective: We validated Food Recognition Assistance and Nudging Insights (FRANI), a mobile artificial intelligence (AI) dietary assessment application in adolescent females aged 12-18 y (n = 36) in Ghana, against weighed records (WR), and multipass 24-hour recalls (24HR).