Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study
| العنوان: | Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study |
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| المؤلفون: | Christophe Matthys, Stephanie Van Asbroeck |
| المصدر: | JMIR Formative Research, Vol 4, Iss 12, p e15602 (2020) JMIR Formative Research |
| بيانات النشر: | JMIR PUBLICATIONS, INC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Dietary assessment, 030309 nutrition & dietetics, Computer science, Accurate estimation, Multiple component, Medicine (miscellaneous), lcsh:Medicine, Health Informatics, Domain (software engineering), 03 medical and health sciences, Upload, 0302 clinical medicine, Computer vision, 030212 general & internal medicine, 0303 health sciences, Original Paper, Food frequency, Application programming interface, accuracy, business.industry, lcsh:R, dietary assessment, Computer Science Applications, automated food recognition, image recognition, Comparison study, Artificial intelligence, business |
| الوصف: | Background In the domain of dietary assessment, there has been an increasing amount of criticism of memory-based techniques such as food frequency questionnaires or 24 hour recalls. One alternative is logging pictures of consumed food followed by an automatic image recognition analysis that provides information on type and amount of food in the picture. However, it is currently unknown how well commercial image recognition platforms perform and whether they could indeed be used for dietary assessment. Objective This is a comparative performance study of commercial image recognition platforms. Methods A variety of foods and beverages were photographed in a range of standardized settings. All pictures (n=185) were uploaded to selected recognition platforms (n=7), and estimates were saved. Accuracy was determined along with totality of the estimate in the case of multiple component dishes. Results Top 1 accuracies ranged from 63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API. None of the platforms were capable of estimating the amount of food. These results demonstrate that certain platforms perform poorly while others perform decently. Conclusions Important obstacles to the accurate estimation of food quantity need to be overcome before these commercial platforms can be used as a real alternative for traditional dietary assessment methods. |
| وصف الملف: | Electronic |
| اللغة: | English |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b5378ecc0135c90a8cbcd67c971d502Test https://lirias.kuleuven.be/handle/123456789/665116Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....0b5378ecc0135c90a8cbcd67c971d502 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |