A Food Intake Estimation System Using an Artificial Intelligence–Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study
The measurement from the food intake estimation system by an AI-based model to estimate leftover liquid food intake in patients showed a high correlation with the actual values with the weighing method. Furthermore, it also showed a higher accuracy than the image visual estimation. The errors of the AI estimation method were within the acceptable range of the weighing method, which indicated that the AI-based food intake estimation system could be applied in clinical environments. However, its lower accuracy than that of direct visual estimation was still an issue.
Authors
- Shan Xiao (ORCID: https://orcid.org/0000-0002-8140-8512)
- Yoshiko Suzuki (ORCID: https://orcid.org/0000-0003-1455-7232)
- Hiroshi Sakaue (ORCID: https://orcid.org/0000-0002-2468-2363)
- Jun Hirose (ORCID: https://orcid.org/0000-0001-9314-8415)
- Masanori Kubota (ORCID: https://orcid.org/0000-0001-9315-3847)
- Kazumi Ozaki (ORCID: https://orcid.org/0000-0001-8433-7636)
- Sosuke Amano (ORCID: https://orcid.org/0000-0001-7463-2631)
- Masato Tagi (ORCID: https://orcid.org/0000-0002-8290-0875)
- Takeshi Konishi
- Yasuhiro Hamada (ORCID: https://orcid.org/0000-0001-9263-5844)
Institutions
- Tokushima University Hospital (JP)
- Tokushima University (JP)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2024-09-19
- DOI
- https://doi.org/10.2196/55218
- Citations
- 9
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 4.89
Funders
- University of Tokushima