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 on 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 few have been validated in LMICs. Obj...
| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/130688 |
| _version_ | 1855527017984819200 |
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| author | Folson, Gloria Bannerman, Boateng Atadze, Vicentia Ador, Gabriel Kolt, Bastien McCloske, 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 |
| author_browse | Ador, Gabriel Arrieta, Alejandra Arsenault, Joanne Atadze, Vicentia Bannerman, Boateng Braga, Bianca C. Doyle, Frank Folson, Gloria Gangupantulu, Rohit Gelli, Aulo Hoang, Nga Thu Hughes, David Kehs, Annalyse Kolt, Bastien McCloske, Peter Nguyen, Phuong Hong Tran, Lan Mai |
| author_facet | Folson, Gloria Bannerman, Boateng Atadze, Vicentia Ador, Gabriel Kolt, Bastien McCloske, 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 |
| author_sort | Folson, Gloria |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background
Important gaps exist on 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 few have been validated in LMICs.
Objective
We validated FRANI (Food Recognition Assistance and Nudging Insights), a mobile Artificial Intelligence (AI) dietary assessment application in adolescent females aged 12–18y (n = 36) in Ghana, against weighed records (WR), and multi-pass 24-hour recalls (24HR).
Methods
Dietary intake was assessed during three 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 energy intake, 15% for five 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, fibre, 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 to WR (21% and 13% respectively).
Conclusions
FRANI AI-assisted dietary assessment could accurately estimate nutrient intake in adolescent females compared to 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 and improve overall nutrient intake estimations. |
| format | Journal Article |
| id | CGSpace130688 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1306882025-04-03T21:29:27Z Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana Folson, Gloria Bannerman, Boateng Atadze, Vicentia Ador, Gabriel Kolt, Bastien McCloske, 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 adolescence (human) artificial intelligence diet nutrient intake women Background Important gaps exist on 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 few have been validated in LMICs. Objective We validated FRANI (Food Recognition Assistance and Nudging Insights), a mobile Artificial Intelligence (AI) dietary assessment application in adolescent females aged 12–18y (n = 36) in Ghana, against weighed records (WR), and multi-pass 24-hour recalls (24HR). Methods Dietary intake was assessed during three 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 energy intake, 15% for five 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, fibre, 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 to WR (21% and 13% respectively). Conclusions FRANI AI-assisted dietary assessment could accurately estimate nutrient intake in adolescent females compared to 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 and improve overall nutrient intake estimations. 2023-08 2023-06-08T18:49:35Z 2023-06-08T18:49:35Z Journal Article https://hdl.handle.net/10568/130688 en Open Access Elsevier Folson, Gloria; Kolt, Bastien; Arrieta, Alejandra; Nguyen, Phuong Hong; Gelli, Aulo; et al. 2023. Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana. Journal of Nutrition 153(8): 2328-2338. https://doi.org/10.1016/j.tjnut.2023.06.001 |
| spellingShingle | adolescence (human) artificial intelligence diet nutrient intake women Folson, Gloria Bannerman, Boateng Atadze, Vicentia Ador, Gabriel Kolt, Bastien McCloske, 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 Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title | Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title_full | Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title_fullStr | Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title_full_unstemmed | Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title_short | Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana |
| title_sort | validation of mobile artificial intelligence technology assisted dietary assessment tool against weighed records and 24 hour recall in adolescent females in ghana |
| topic | adolescence (human) artificial intelligence diet nutrient intake women |
| url | https://hdl.handle.net/10568/130688 |
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