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Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.
Wang, Xue; Liu, Yan; Rong, Zhiqin; Wang, Weijia; Han, Meifen; Chen, Moxi; Fu, Jin; Chong, Yuming; Long, Xiao; Tang, Yong; Chen, Wei.
Afiliação
  • Wang X; Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Liu Y; Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Rong Z; Genesis Artificial Intelligence Laboratory, Futong Technology, Chengdu, China.
  • Wang W; Genesis Artificial Intelligence Laboratory, Futong Technology, Chengdu, China.
  • Han M; Department of Pharmacy, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
  • Chen M; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
  • Fu J; Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Chong Y; Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Long X; Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing, China.
  • Tang Y; Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing, China.
  • Chen W; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
JPEN J Parenter Enteral Nutr ; 48(5): 554-561, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38796717
ABSTRACT

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.
Assuntos
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento Tridimensional / Desnutrição / Face / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento Tridimensional / Desnutrição / Face / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article