{"title":"利用三维面部信息预测营养不良及其并发症。","authors":"Xue Wang, Weijia Wang, Moxi Chen, Meifen Han, Zhiqin Rong, Jin Fu, Yuming Chong, Nanze Yu, Xiao Long, Zhitao Cheng, Yong Tang, Wei Chen","doi":"10.1002/ncp.11215","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL).</p><p><strong>Methods: </strong>From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset.</p><p><strong>Results: </strong>Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74.</p><p><strong>Conclusion: </strong>Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.</p>","PeriodicalId":19354,"journal":{"name":"Nutrition in Clinical Practice","volume":" ","pages":"1354-1363"},"PeriodicalIF":2.1000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using 3D facial information to predict malnutrition and consequent complications.\",\"authors\":\"Xue Wang, Weijia Wang, Moxi Chen, Meifen Han, Zhiqin Rong, Jin Fu, Yuming Chong, Nanze Yu, Xiao Long, Zhitao Cheng, Yong Tang, Wei Chen\",\"doi\":\"10.1002/ncp.11215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL).</p><p><strong>Methods: </strong>From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset.</p><p><strong>Results: </strong>Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74.</p><p><strong>Conclusion: </strong>Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.</p>\",\"PeriodicalId\":19354,\"journal\":{\"name\":\"Nutrition in Clinical Practice\",\"volume\":\" \",\"pages\":\"1354-1363\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nutrition in Clinical Practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/ncp.11215\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition in Clinical Practice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/ncp.11215","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/25 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
Using 3D facial information to predict malnutrition and consequent complications.
Background: Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL).
Methods: From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital. Subjective global assessment was used as the gold standard of malnutrition diagnosis. The highest Youden index value was selected as the PhA cutoff point. We developed a multimodal DL framework to automatically analyze the three-dimensional (3D) facial data and accurately determine patients' PhA categories. The framework was trained and validated using a cross-validation approach and tested on an independent dataset.
Results: Four hundred eighty-two patients were included in the final dataset, including 176 with malnourishment. In male patients, the PhA value with the highest Youden index was 5.55°, and the area under the receiver operating characteristic curve (AUC) = 0.68; in female patients, the PhA value with the highest Youden index was 4.88°, and AUC = 0.69. Inpatients with low PhA had higher incidence of infectious complications during the hospital stay (P = 0.003). The DL model trained with 4096 points extracted from 3D facial data had the best performance. The algorithm showed fair performance in predicting PhA, with an AUC of 0.77 and an accuracy of 0.74.
Conclusion: Predicting the PhA of inpatients from facial images is feasible and can be used for malnutrition assessment and prognostic prediction.
期刊介绍:
NCP is a peer-reviewed, interdisciplinary publication that publishes articles about the scientific basis and clinical application of nutrition and nutrition support. NCP contains comprehensive reviews, clinical research, case observations, and other types of papers written by experts in the field of nutrition and health care practitioners involved in the delivery of specialized nutrition support. This journal is a member of the Committee on Publication Ethics (COPE).