Artificial intelligence-assisted dietary assessment in adolescent girls in Sri Lanka: Validity against weighed food records and comparison with 24-hour recalls
Background
Reliable dietary data for adolescents in low- and middle-income countries (LMICs) are limited due to high costs and estimation errors in traditional dietary assessment methods. Although technology-assisted dietary assessment tools are becoming popular, few have been validated in LMICs.
Objectives
This study validated the PlantVillage Food Recognition Assistance and Nudging Insights (FRANI), an artificial intelligence-assisted mobile application for dietary assessment, against weighed food records (WFR) and multipass 24-h recalls (24HR) among adolescent girls aged 14‒18 y (n = 60) in urban/semi-urban communities in Sri Lanka.
Methods
Dietary intake was assessed over 2 non-consecutive days using 3 methods: FRANI, WFR, and 24HR. The equivalence of nutrient intake was evaluated using mixed-effect models accounting for repeated measures by comparing intake ratios (FRANI/WFR and 24HR/WFR) with 10%, 15%, and 20% equivalence bounds. The concordance correlation coefficient was utilized to assess the agreement between methods.
Results
FRANI demonstrated equivalence with WFR at the 10% bound for energy and vitamin A; 15% for protein, fiber, iron, and zinc; and 20% for fat, niacin, and folate intakes. Comparisons between 24HR and WFR found that no nutrients fell within the 10% bound. Energy, protein, fat, iron, niacin, and vitamin A intakes were equivalent at 15% bound, whereas fiber, calcium, folate, and vitamin C intakes were equivalent at 20% bound. Concordance correlation coefficient ranged from 0.49 to 0.89 for FRANI compared to WFR, and 0.44 to 0.84 for 24HR compared with WFR. Omission errors were 2% for FRANI and 12% for 24HR, and intrusion errors were 7% and 9%, respectively.
Conclusions
PlantVillage FRANI application accurately estimated nutrient intakes of adolescent girls in Sri Lanka compared to the WFR. Its performance was at least comparable to the traditional 24HR method, supporting its potential as a scalable alternative for dietary assessment in similar LMIC populations.
Authors
Karunarathna, Nilmini; Perera, Thushanthi; Nwabuikwu, Odiche; Bannerman, Boateng; Bempong, Silas; McCloskey, Peter; Nguyen, Phuong Hong; Hughes, David; Folson, Gloria; Silva, Renuka; Gelli, Aulo
Citation
Karunarathna, Nilmini; Perera, Thushanthi; Nwabuikwu, Odiche; Bannerman, Boateng; Bempong, Silas; et al. 2026. Artificial intelligence-assisted dietary assessment in adolescent girls in Sri Lanka: Validity against weighed food records and comparison with 24-hour recalls. Journal of Nutrition 156(5): 101476. https://doi.org/10.1016/j.tjnut.2026.101476
Keywords
Asia; Southern Asia; Artificial Intelligence; Dietary Assessment; Adolescents; Gender
Record type
Journal Article
