1
|
Li X, Li L, Wei J, Zhang P, Turchenko V, Vempala N, Kabakov E, Habib F, Gupta A, Huang H, Lee K. Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images. BIOMED RESEARCH INTERNATIONAL 2024; 2024:5551209. [PMID: 39118805 PMCID: PMC11309814 DOI: 10.1155/2024/5551209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/15/2024] [Accepted: 07/04/2024] [Indexed: 08/10/2024]
Abstract
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.
Collapse
Affiliation(s)
- Xiaoyan Li
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
| | - Li Li
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Jing Wei
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Pengwei Zhang
- Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, China
| | | | | | | | - Faisal Habib
- Mathematics, Analytics, and Data Science LabFields Institute for Research in Mathematical Sciences, Toronto, Ontario, Canada
| | - Arvind Gupta
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
| | - Huaxiong Huang
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
- Mathematics and StatisticsYork University, Toronto, Ontario, Canada
| | - Kang Lee
- Computer ScienceUniversity of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Kusakunniran W, Imaromkul T, Mongkolluksamee S, Thongkanchorn K, Ritthipravat P, Tuakta P, Benjapornlert P. Deep Upscale U-Net for automatic tongue segmentation. Med Biol Eng Comput 2024; 62:1751-1762. [PMID: 38372910 DOI: 10.1007/s11517-024-03051-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 02/13/2024] [Indexed: 02/20/2024]
Abstract
In a treatment or diagnosis related to oral health conditions such as oral cancer and oropharyngeal cancer, an investigation of tongue's movements is a major part. In an automatic measurement of such movement, it must first start with a task of tongue segmentation. This paper proposes a solution of tongue segmentation based on a decoder-encoder CNN-based structure i.e., U-Net. However, it could suffer from a problem of feature loss in deep layers. This paper proposes a Deep Upscale U-Net (DU-UNET). An additional up-sampling of the feature map from a contracting path is concatenated to an upper layer of an expansive path, based on an original U-Net structure. The segmentation model is constructed by training DU-UNET on the two publicly available datasets, and transferred to the self-collected dataset of tongue images with five tongue postures which were recorded at a far distance from a camera under a real-world scenario. The proposed DU-UNET outperforms the other existing methods in our literature reviews, with accuracy of 99.2%, mean IoU of 97.8%, Dice score of 96.8%, and Jaccard score of 96.8%.
Collapse
Affiliation(s)
- Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, 73170, Nakhon Pathom, Thailand.
| | - Thanandon Imaromkul
- Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, 73170, Nakhon Pathom, Thailand
| | - Sophon Mongkolluksamee
- Department of Computer Science, Faculty of Science, Srinakharinwirot University, 114 Sukhumvit 23, 10110, Bangkok, Thailand
| | - Kittikhun Thongkanchorn
- Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, 73170, Nakhon Pathom, Thailand
| | - Panrasee Ritthipravat
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, 999 Phuttamonthon 4 Road, Salaya, 73170, Nakhon Pathom, Thailand.
| | - Pimchanok Tuakta
- Department of Rehabilitation Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama 6 Road, 10400, Bangkok, Thailand
| | - Paitoon Benjapornlert
- Department of Rehabilitation Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 270 Rama 6 Road, 10400, Bangkok, Thailand
| |
Collapse
|
3
|
Chen J, Sun Y, Li J, Lyu M, Yuan L, Sun J, Chen S, Hu C, Wei Q, Xu Z, Guo T, Cheng X. In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study. MICROBIOME 2024; 12:6. [PMID: 38191439 PMCID: PMC10773145 DOI: 10.1186/s40168-023-01730-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 11/21/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Our previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer. METHODS We employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins. RESULTS We measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort. CONCLUSIONS We characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer. Video Abstract.
Collapse
Affiliation(s)
- Jiahui Chen
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Yingying Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Jie Li
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
| | - Mengge Lyu
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, China
| | - Li Yuan
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Jiancheng Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shangqi Chen
- Department of General Surgery, HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo, China
| | - Can Hu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Qing Wei
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Zhiyuan Xu
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
- School of Medicine, School of Life Sciences, Westlake University, Hangzhou, China.
- Research Center for Industries of the Future, Westlake University, Hangzhou, China.
| | - Xiangdong Cheng
- Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
- Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China.
| |
Collapse
|
4
|
Encoder-decoder network with RMP for tongue segmentation. Med Biol Eng Comput 2023; 61:1193-1207. [PMID: 36692799 DOI: 10.1007/s11517-022-02761-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 12/26/2022] [Indexed: 01/25/2023]
Abstract
Tongue and its movements can be used for several medical-related tasks, such as identifying a disease and tracking a rehabilitation. To be able to focus on a tongue region, the tongue segmentation is needed to compute a region of interest for a further analysis. This paper proposes an encoder-decoder CNN-based architecture for segmenting a tongue in an image. The encoder module is mainly used for the tongue feature extraction, while the decoder module is used to reconstruct a segmented tongue from the extracted features based on training images. In addition, the residual multi-kernel pooling (RMP) is also applied into the proposed network to help in encoding multiple scales of the features. The proposed method is evaluated on two publicly available datasets under a scenario of front view and one tongue posture. It is then tested on a newly collected dataset of five tongue postures. The reported performances show that the proposed method outperforms existing methods in the literature. In addition, the re-training process could improve applying the trained model on unseen dataset, which would be a necessary step of applying the trained model on the real-world scenario.
Collapse
|
5
|
Chen H, Li Q, Li M, Liu S, Yao C, Wang Z, Zhao Z, Liu P, Yang F, Li X, Wang J, Zeng Y, Tong X. Microbial characteristics across different tongue coating types in a healthy population. J Oral Microbiol 2021; 13:1946316. [PMID: 34367522 PMCID: PMC8317956 DOI: 10.1080/20002297.2021.1946316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background The physical appearance of tongue coatings is vital for traditional Chinese medicine (TCM) to diagnose health and disease status. The microbiota of different tongue coatings could also influence coating formation and be further associated with specific diseases. Previous studies have focused on bacteria from different tongue coatings in the context of specific diseases, but the normal variations in healthy individuals remain unknown.Aim: We examined the tongue microbiota by metagenomics in 94 healthy individuals classified into eight different tongue types.Results: The overall composition of the tongue coating microbiome is not drastically different among different coating types, similar to the findings of previous studies in healthy populations. Further analysis revealed microbiota characteristics of each coating type, and many of the key bacteria are reported to be implicated in diseases. Moreover, further inclusion of diabetic patients revealed disease-specific enrichment of Capnocytophaga, even though the same tongue coatings were studied.Conclusions: This work revealed the characteristic compositions of distinctive tongue coatings in a healthy population, which serves as a basis for understanding the tongue coating formation mechanism and provides a valuable reference to further investigate disease-specific tongue coating bacterial markers.
Collapse
Affiliation(s)
- Hairong Chen
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Qingwei Li
- Departments of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Min Li
- Departments of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Sheng Liu
- Departments of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chensi Yao
- Departments of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zixiong Wang
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhuoya Zhao
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Ping Liu
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Fan Yang
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xinjian Li
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jun Wang
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Yixin Zeng
- CAS Key Laboratory of Infection and Immunity, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Xiaolin Tong
- Departments of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| |
Collapse
|