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

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2024-12-05 DOI:10.1038/s41746-024-01346-8

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

{"title":"AI-enabled wearable cameras for assisting dietary assessment in African populations","authors":"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","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-16"},"PeriodicalIF":12.4000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01346-8.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Digital Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41746-024-01346-8","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

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.

Abstract Image

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.