Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images.
The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CN...
| Publicado en: | BioMed Research International Vol. 2024; pp. 1 - 17 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/1/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179674140&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179674140 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/1/2024 vid: 2024 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 179674140 179674140 179674140 10.1155/2024/5551209 179674140 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images. aug: au: Li, Xiaoyan Li, Li Wei, Jing Zhang, Pengwei Turchenko, Volodymyr Vempala, Naresh Kabakov, Evgueni Habib, Faisal Gupta, Arvind Huang, Huaxiong Lee, Kang Ahuja, Vishal affil: Hangzhou Normal University Affiliated Hospital,, Hangzhou, Zhejiang,, China sug: subj: Convolutional Neural Networks Utilization Tongue Anatomy and Histology Tongue Physiology Prediction Models Aging Sex Determination Body Weight Human Female Male Predictive Value of Tests Pearson's Correlation Coefficient ROC Curve Sensitivity and Specificity Descriptive Statistics Funding Source Female Male ab: The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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