AI-enabled wearable cameras for assisting dietary assessment in African populations

Frank P. -W. Lo; Jianing Qiu; Modou L. Jobarteh; Yingnan Sun; Zeyu Wang; Shuo Jiang; Tom Baranowski; Alex K. Anderson; Megan A. Mccrory; Edward Sazonov; Wenyan Jia; Mingui Sun; Matilda Steiner-Asiedu; Gary Frost; Benny Lo

doi:10.1038/s41746-024-01346-8

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AI-enabled wearable cameras for assisting dietary assessment in African populations

Journal article

Open access

Peer reviewed

AI-enabled wearable cameras for assisting dietary assessment in African populations

Frank P. -W. Lo, Jianing Qiu, Modou L. Jobarteh, Yingnan Sun, Zeyu Wang, Shuo Jiang, Tom Baranowski, Alex K. Anderson, Megan A. Mccrory, Edward Sazonov, …

NPJ digital medicine, Vol.7(1), 356

2024

DOI: https://doi.org/10.1038/s41746-024-01346-8

PMID: 39638852

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Abstract

Health Care Sciences & Services

Life Sciences & Biomedicine

Medical Informatics

Science & Technology

We have developed a population-level method for dietary assessment using low-cost wearable cameras. Our approach, EgoDiet, employs an egocentric vision-based pipeline to learn portion sizes, addressing the shortcomings of traditional self-reported dietary methods. To evaluate the functionality of this method, field studies were conducted in London (Study A) and Ghana (Study B) among populations of Ghanaian and Kenyan origin. In Study A, EgoDiet's estimations were contrasted with dietitians' assessments, revealing a performance with a Mean Absolute Percentage Error (MAPE) of 31.9% for portion size estimation, compared to 40.1% for estimates made by dietitians. We further evaluated our approach in Study B, comparing its performance to the traditional 24-Hour Dietary Recall (24HR). Our approach demonstrated a MAPE of 28.0%, showing a reduction in error when contrasted with the 24HR, which exhibited a MAPE of 32.5%. This improvement highlights the potential of using passive camera technology to serve as an alternative to the traditional dietary assessment methods.

Details

Title: AI-enabled wearable cameras for assisting dietary assessment in African populations
Authors/Creators: Frank P. -W. Lo - NIHR Imperial Biomedical Research Centre
Jianing Qiu - Hammersmith Hospital
Modou L. Jobarteh - London School of Hygiene & Tropical Medicine
Yingnan Sun - Imperial College London
Zeyu Wang - Imperial College London
Shuo Jiang - Tongji University
Tom Baranowski - Children's Nutrition Research Center at Baylor College of Medicine
Alex K. Anderson - University of Georgia
Megan A. Mccrory - Boston University
Edward Sazonov - University of Alabama
Wenyan Jia - University of Pittsburgh
Mingui Sun - University of Pittsburgh
Matilda Steiner-Asiedu - University of Ghana
Gary Frost - Imperial College London
Benny Lo - Imperial College London
Publication Details: NPJ digital medicine, Vol.7(1), 356
Publisher: Springer Nature
Number of pages: 16
Grant note: OPP1171395 / Bill and Melinda Gates Foundation; Bill & Melinda Gates Foundation Bill & Melinda Gates Foundation; Bill & Melinda Gates Foundation Grand Challenges Explorations Initiative; CGIAR OPP1171395 / Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)
Identifiers: 991005884955107891
Murdoch Affiliation: Centre for Computational and Systems Medicine
Language: English
Resource Type: Journal article

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13 Times Cited - Web of Science

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.44 Nutrition & Health Behavior; 1.44.29 Nutrition and Obesity
Web Of Science research areas: Health Care Sciences & Services; Medical Informatics
ESI research areas: Clinical Medicine