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Updated: Jun 25, 2025
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points
Xue Wang1, Yan Liu1, Zhiqin Rong2
1Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud.
Methods:
A group of 482 patients were studied in this perspective work. The 3D facial data were obtained using a 3D camera and represented as a 3D facial points cloud. A DL model, PointNet++, was trained and evaluated using the points cloud as inputs and classified the malnutrition states. The performance was evaluated with the area under the receiver operating characteristic curve, accuracy, specificity, sensitivity, and F1 score.
Results:
Among the 482 patients, 150 patients (31.1%) were diagnosed as having moderate malnutrition and 54 patients (11.2%) as having severe malnutrition. The DL model achieved the performance with an area under the receiver operating characteristic curve of 0.7240 ± 0.0416.
Conclusion:
The DL model achieved encouraging performance in accurately classifying nutrition states based on a points cloud of 3D facial information of patients with malnutrition.
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