Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Diet modification is a mainstay for the successful management of metabolic syndrome and potentially may reduce the risk of cardiovascular disease. Accurate estimation of essential nutrients in daily meals is currently challenging to quantify. HAKARIUM (AstraZeneca Co., Ltd., Osaka, Japan) is a recently introduced artificial intelligence (AI)-based application that can estimate each nutrient component through photographs, although its applicability to real-world practice remains unknown.
Methods: Lunchtime meals served for healthy individuals at a single university cooperative society between September 2023 and February 2024 were analyzed. Nutrient components, including energy in the form of calories, protein, and salts, were estimated by the HAKARIUM application and compared with the actual nutrient values that were officially calculated and presented by the university cooperative society.
Results: A total of 62 meals were included. Actual values of energy, protein, and salt content per meal were 382 (358, 431) kcal, 17.1 (13.9, 18.9) g, and 2.9 (2.6, 3.1) g, respectively. AI-estimated values of energy, protein, and salt content per meal were 636 (493, 835) kcal, 25.7 (19.7, 36.3) g, and 4.2 (3.5, 4.6) g, respectively. Most of the values were within the limits of agreement with significant correlations between the two variables, respectively (r > 0.80, p < 0.05 for all).
Conclusion: AI-based estimation of nutrient components had relatively good agreement with actually calculated values.
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Source |
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http://dx.doi.org/10.1016/j.jjcc.2024.10.003 | DOI Listing |
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