Chest X-ray based AI may accurately predict malnutrition risk among bedridden patients

The key results of the study are:

• The correlation coefficients between the predicted and measured values using the height prediction model were R=0.855 and R=0.81 for males and females, respectively.
• The correlation coefficients between the values predicted by the weight prediction model and measured values were R=0.793 and R=0.86, respectively.

They said, “Our chest radiographic prediction model highly correlates with actual height and weight. It can be combined with information regarding clinical nutrition factors to assess malnutrition risk rapidly.”

Training the prediction model using chest radiographs from each hospital can be optimized for the most common ethnic groups in the area, they noted. The model illustrates the potential of automated imaging AI for proper nutrition prediction models in older adults.

Reference:

Nakao, Y., Sasaki, R., Mawatari, F., Harakawa, K., Okita, M., Mitsutake, N., & Nakao, K. (2023). Development of Deep-Learning Tool to Predict Appropriate Height and Weight from Chest Radiographs in Bedridden Patients. Clinical Nutrition Open Science. https://doi.org/10.1016/j.nutos.2023.08.005