JMIR Publications

ABSTRACT

Background:

The accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable.

Objective:

The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation.

Methods:

The accuracy of obtained using the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food, and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. Welch's t-test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation.

Results:

The mean absolute errors obtained through the AI estimation approach for side dishes were as follows: fermented milk: 0.63 and peach juice: 0.25. These were significantly smaller than those obtained using the visual estimation approach: fermented milk: 1.40 and peach juice: 0.90. Contrastingly, the mean absolute error for staple food obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for staple foods showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers.

Conclusions:

AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement.