1. Validation of mobile artificial intelligence technology–assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana
- Author
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Sustainable Healthy Diets, Folson, Gloria; Bannerman, Boateng; Atadze, Vicentia; Ador, Gabriel; Kolt, Bastien; McCloskey, Peter; Gangupantulu, Rohit; Arrieta, Alejandra; Braga, Bianca C.; Arsenault, Joanne; Kehs, Annalyse; Doyle, Frank; Tran, Lan Mai; Hoang, Nga Thu; Hughes, David; Nguyen, Phuong Hong; Gelli, Aulo, http://orcid.org/0000-0003-3418-1674 Nguyen, Phuong Hong; http://orcid.org/0000-0003-4977-2549 Gelli, Aulo, Sustainable Healthy Diets, Folson, Gloria; Bannerman, Boateng; Atadze, Vicentia; Ador, Gabriel; Kolt, Bastien; McCloskey, Peter; Gangupantulu, Rohit; Arrieta, Alejandra; Braga, Bianca C.; Arsenault, Joanne; Kehs, Annalyse; Doyle, Frank; Tran, Lan Mai; Hoang, Nga Thu; Hughes, David; Nguyen, Phuong Hong; Gelli, Aulo, and http://orcid.org/0000-0003-3418-1674 Nguyen, Phuong Hong; http://orcid.org/0000-0003-4977-2549 Gelli, Aulo
- Abstract
PR, IFPRI3; ISI; CRP4; Capacity Strengthening; DCA; 2 Promoting Healthy Diets and Nutrition for all; Nudging for Good, Nutrition, Diets, and Health (NDH); Food and Nutrition Policy; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), 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). Methods Dietary intake was assessed during 3 nonconsecutive 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 energy intake, 15% for 5 nutrients (iron, zinc, folate, niacin, and vitamin B6), and 20% for protein, calcium, riboflavin, and thiamine intakes. Comparisons between 24HR and WR estimated equivalence at the 20% bound for energy, carbohydrate, fiber, calcium, thiamine, and vitamin A intakes. The CCCs by nutrient between FRANI and WR ranged between 0.30 and 0.68, which was similar for CCC between 24HR and WR (ranging between 0.38 and 0.67). Comparisons of food consumption episodes from FRANI and WR found 31% omission and 16% intrusion errors. Omission and intrusion errors were lower when comparing 24HR with WR (21% and 13%, respectively). Conclusions FRANI AI–assisted dietary assessment could accurately estimate nutrient intake in adolescent females compared with WR in urban Ghana. FRANI estimates were at least as accurate as those provided through 24HR. Further improvements in food recognition and portion estimation in FRANI could reduce errors an
- Published
- 2023