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Bae H, Park SY, Kim CE. A practical guide to implementing artificial intelligence in traditional East Asian medicine research. Integr Med Res 2024; 13:101067. [PMID: 39253696 PMCID: PMC11381867 DOI: 10.1016/j.imr.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/11/2024] Open
Abstract
In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sa-Yoon Park
- Department of Physiology, College of Korean Medicine, Wonkwang University, Iksan, Korea
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Korea
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Duan M, Mao B, Li Z, Wang C, Hu Z, Guan J, Li F. Feasibility of tongue image detection for coronary artery disease: based on deep learning. Front Cardiovasc Med 2024; 11:1384977. [PMID: 39246581 PMCID: PMC11377252 DOI: 10.3389/fcvm.2024.1384977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 08/07/2024] [Indexed: 09/10/2024] Open
Abstract
Aim Clarify the potential diagnostic value of tongue images for coronary artery disease (CAD), develop a CAD diagnostic model that enhances performance by incorporating tongue image inputs, and provide more reliable evidence for the clinical diagnosis of CAD, offering new biological characterization evidence. Methods We recruited 684 patients from four hospitals in China for a cross-sectional study, collecting their baseline information and standardized tongue images to train and validate our CAD diagnostic algorithm. We used DeepLabV3 + for segmentation of the tongue body and employed Resnet-18, pretrained on ImageNet, to extract features from the tongue images. We applied DT (Decision Trees), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), and XGBoost models, developing CAD diagnostic models with inputs of risk factors alone and then with the additional inclusion of tongue image features. We compared the diagnostic performance of different algorithms using accuracy, precision, recall, F1-score, AUPR, and AUC. Results We classified patients with CAD using tongue images and found that this classification criterion was effective (ACC = 0.670, AUC = 0.690, Recall = 0.666). After comparing algorithms such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and XGBoost, we ultimately chose XGBoost to develop the CAD diagnosis algorithm. The performance of the CAD diagnosis algorithm developed solely based on risk factors was ACC = 0.730, Precision = 0.811, AUC = 0.763. When tongue features were integrated, the performance of the CAD diagnosis algorithm improved to ACC = 0.760, Precision = 0.773, AUC = 0.786, Recall = 0.850, indicating an enhancement in performance. Conclusion The use of tongue images in the diagnosis of CAD is feasible, and the inclusion of these features can enhance the performance of existing CAD diagnosis algorithms. We have customized this novel CAD diagnosis algorithm, which offers the advantages of being noninvasive, simple, and cost-effective. It is suitable for large-scale screening of CAD among hypertensive populations. Tongue image features may emerge as potential biomarkers and new risk indicators for CAD.
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Affiliation(s)
- Mengyao Duan
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Boyan Mao
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
| | - Zijian Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Chuhao Wang
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
| | - Zhixi Hu
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Jing Guan
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Feng Li
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
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Wu A, Hu T, Lai C, Zeng Q, Luo L, Shu X, Huang P, Wang Z, Feng Z, Zhu Y, Cao Y, Li Z. Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations. Cancer Med 2024; 13:e70153. [PMID: 39206620 PMCID: PMC11358765 DOI: 10.1002/cam4.70153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Revised: 08/06/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. METHODS Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. RESULTS We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. CONCLUSIONS The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.
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Affiliation(s)
- Ahao Wu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
- Medical Innovation CentreThe First Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
| | - Tengcheng Hu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Chao Lai
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Qingwen Zeng
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Lianghua Luo
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Xufeng Shu
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Pan Huang
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zhonghao Wang
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zongfeng Feng
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Yanyan Zhu
- Department of RadiologyThe First Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
| | - Yi Cao
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
| | - Zhengrong Li
- Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical CollegeNanchang UniversityNanchangJiangxiChina
- Department of Digestive Surgery, Digestive Disease HospitalThe Third Affiliated Hospital of Nanchang UniversityNanchangJiangxiChina
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You Z, Lu J, Xu Y, Zhang R, Zhu Z, Wang Y, Hao Y. The metabolites mainly composed of lipids in tongue coating are non-invasive potential biomarkers for chronic gastritis. Sci Rep 2024; 14:17574. [PMID: 39079952 PMCID: PMC11289369 DOI: 10.1038/s41598-024-68261-6] [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: 04/29/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
The changes in tongue coating metabolites in patients with chronic gastritis (CG) under different gastroscopy indicators were analyzed, and these metabolites were screened for potential non-invasive biomarkers to assist in the diagnosis of chronic gastritis. The technology of gas chromatography and liquid chromatography combined with mass spectrometry has been used to more comprehensively detect tongue coating metabolites of 350 CG patients. Spearman correlation analysis and random forest algorithm were used to screen metabolites that can serve as potential biomarkers. Compared with healthy individuals, CG group showed significant changes in the content of 101 metabolites, with an increase in the content of 54 metabolites and a decrease in the content of 47 metabolites. These differential metabolites are mainly composed of 47 lipids and lipid like substances. 1 metabolite was associated with bile reflux, 1 metabolite was associated with gastric mucosal erosion, 10 metabolites were associated with atrophy, 10 metabolites were associated with intestinal metaplasia, and 3 metabolites were associated with Helicobacter pylori infection. The ROC model composed of 5 metabolites can distinguish between CG group and healthy individuals, with an accuracy of 95.4%. The ROC model composed of 5,6-Dihydroxyindole can distinguish between chronic superficial gastritis group and chronic atrophic gastritis group, with an accuracy of 75.3%. The lipids and lipid like metabolites were the main abnormal metabolites in patients with chronic gastritis. It was worth noting that the content of Sphinganine 1-phase, 4-Ipomenol, and Nervonic acid in tongue coating increased, and the content of 1-Methyladenosine and 3-Hydroxycapric acid decreased, which helped to identify CG patients. The decrease in the content of 5,6-dihydroxyindole reminded patients that the development trend of CG was shifting from superficial to atrophic or even intestinal metaplasia. The detection of these metabolic markers of tongue coating was expected to be developed as a non-invasive and convenient technology in the future to assist us in monitoring and diagnosing the occurrence and development of CG.
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Affiliation(s)
- Zhiyuan You
- Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China
| | - Junhong Lu
- Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China
| | - Yifeng Xu
- Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China
| | - Renling Zhang
- Longhua Hospital Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, China
| | - Zhujing Zhu
- Longhua Hospital Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Shanghai, China
| | - Yiqin Wang
- Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China
| | - Yiming Hao
- Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China.
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Ge J, Li M, Yao J, Guo J, Li X, Li G, Han X, Li Z, Liu M, Zhao J. The potential of EGCG in modulating the oral-gut axis microbiota for treating inflammatory bowel disease. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 130:155643. [PMID: 38820660 DOI: 10.1016/j.phymed.2024.155643] [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: 02/24/2024] [Revised: 04/07/2024] [Accepted: 04/13/2024] [Indexed: 06/02/2024]
Abstract
Inflammatory bowel disease (IBD) is a recurrent chronic intestinal disorder that includes ulcerative colitis (UC) and Crohn's disease (CD). Its pathogenesis involves intricate interactions between pathogenic microorganisms, native intestinal microorganisms, and the intestinal immune system via the oral-gut axis. The strong correlation observed between oral diseases and IBD indicates the potential involvement of oral pathogenic microorganisms in IBD development. Consequently, therapeutic strategies targeting the proliferation, translocation, intestinal colonization and exacerbated intestinal inflammation of oral microorganisms within the oral-gut axis may partially alleviate IBD. Tea consumption has been identified as a contributing factor in reducing IBD, with epigallocatechin gallate (EGCG) being the primary bioactive compound used for IBD treatment. However, the precise mechanism by which EGCG mediates microbial crosstalk within the oral-gut axis remains unclear. In this review, we provide a comprehensive overview of the diverse oral microorganisms implicated in the pathogenesis of IBD and elucidate their colonization pathways and mechanisms. Subsequently, we investigated the antibacterial properties of EGCG and its potential to attenuate microbial translocation and colonization in the gut, emphasizing its role in attenuating exacerbations of IBD. We also elucidated the toxic and side effects of EGCG. Finally, we discuss current strategies for enhancing EGCG bioavailability and propose novel multi-targeted nano-delivery systems for the more efficacious management of IBD. This review elucidates the role and feasibility of EGCG-mediated modulation of the oral-gut axis microbiota in the management of IBD, contributing to a better understanding of the mechanism of action of EGCG in the treatment of IBD and the development of prospective treatment strategies.
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Affiliation(s)
- Jiaming Ge
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Mengyuan Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jingwen Yao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Jinling Guo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Xiankuan Li
- Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; College of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Gang Li
- State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China
| | - Xiangli Han
- Department of Geriatric, Fourth Teaching Hospital of Tianjin University of TCM, Tianjin 300450, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Ming Liu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, 236 Baidi Road, Nankai District, Tianjin 300192, China.
| | - Jing Zhao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
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Wang L, Zhang Q, Zhang P, Wu B, Chen J, Gong J, Tang K, Du S, Li S. Development of an artificial intelligent model for pre-endoscopic screening of precancerous lesions in gastric cancer. Chin Med 2024; 19:90. [PMID: 38951913 PMCID: PMC11218324 DOI: 10.1186/s13020-024-00963-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 06/18/2024] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. METHODS From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. RESULTS A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for overall PLGC (95% confidence interval (CI) 0.71-0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82-0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone. In addition, AITonguequiry has superior performance compared to existing PLGC screening methodologies, with the AUC value enhancing 45% in terms of PLGC screening (0.74 vs. 0.51, p < 0.05) and 52% in terms of high-risk PLGC screening (0.82 vs. 0.54, p < 0.05). In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. CONCLUSION Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden.
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Affiliation(s)
- Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Qian Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Bowen Wu
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Jun Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Jiamin Gong
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Kaiqiang Tang
- Department of Control Science and Intelligence Engineering, Nanjing University, Nanjing, China
| | - Shiyu Du
- Department of Gastroenterology, China-Japan Friendship Hospital, Chaoyang District, Beijing, China.
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
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Zhai Y, Lan D, Lv S, Mo L. Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients. Front Med (Lausanne) 2024; 11:1399527. [PMID: 38933112 PMCID: PMC11200536 DOI: 10.3389/fmed.2024.1399527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Objective The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation. Methods In this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models' performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon's index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques. Results A subset of the study cohort's patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874-0.904), precision score of 0.871 (0.849-0.893), recall score of 0.913 (0.890-0.936), F1 score of 0.891 (0.876-0.906), and AUC of 0.956 (0.939-0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model. Conclusion By identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.
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Affiliation(s)
| | | | | | - Liqin Mo
- Cardiothoracic Surgery Intensive Care Unit, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Pan D, Guo Y, Fan Y, Wan H. Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2024; 52:605-623. [PMID: 38715181 DOI: 10.1142/s0192415x24500265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM.
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Affiliation(s)
- Danping Pan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yilei Guo
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yongfu Fan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Haitong Wan
- School of Basic Medicine Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
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Tiryaki B, Torenek-Agirman K, Miloglu O, Korkmaz B, Ozbek İY, Oral EA. Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network. BMC Med Imaging 2024; 24:59. [PMID: 38459518 PMCID: PMC10924407 DOI: 10.1186/s12880-024-01234-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/22/2024] [Indexed: 03/10/2024] Open
Abstract
OBJECTIVE This study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs). METHODS A dataset consisting of five classes, four tongue lesion classes (coated, geographical, fissured tongue, and median rhomboid glossitis), and one healthy/normal tongue class, was constructed using tongue images of 623 patients who were admitted to our clinic. Classification performance was evaluated on VGG19, ResNet50, ResNet101, and GoogLeNet networks using fusion based majority voting (FBMV) approach for the first time in the literature. RESULTS In the binary classification problem (normal vs. tongue lesion), the highest classification accuracy performance of 93,53% was achieved utilizing ResNet101, and this rate was increased to 95,15% with the application of the FBMV approach. In the five-class classification problem of tongue lesion types, the VGG19 network yielded the best accuracy rate of 83.93%, and the fusion approach improved this rate to 88.76%. CONCLUSION The obtained test results showed that tongue lesions could be identified with a high accuracy by applying DCNNs. Further improvement of these results has the potential for the use of the proposed method in clinic applications.
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Affiliation(s)
- Burcu Tiryaki
- Department of Electrical Electronic Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey
| | - Kubra Torenek-Agirman
- Department of Oral Diagnosis and Dentomaxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - Ozkan Miloglu
- Department of Oral Diagnosis and Dentomaxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey.
- Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, 25240, Turkey.
| | - Berfin Korkmaz
- Department of Oral Diagnosis and Dentomaxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - İbrahim Yucel Ozbek
- Department of Electrical Electronic Engineering (High Performance Comp Applicat & Res Ctr), Ataturk University, Erzurum, Turkey
| | - Emin Argun Oral
- Department of Electrical Electronic Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey
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Zhou E, Shen Q, Hou Y. Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review. Front Pharmacol 2024; 15:1181183. [PMID: 38464717 PMCID: PMC10921893 DOI: 10.3389/fphar.2024.1181183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 03/12/2024] Open
Abstract
Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.
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Affiliation(s)
- E. Zhou
- Yuhu District Healthcare Security Administration, Xiangtan, China
| | - Qin Shen
- Department of Respiratory Medicine, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Yang Hou
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China
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11
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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12
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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.
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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.
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Shixiong Z, Shaowei L, Zeqi Y, Miaochan X, Pingping Z, Haiyan B, Jingjing L, Yangang W. Study on the Characteristics of Traditional Chinese Medicine Syndromes in Patients with Erosive Gastritis Based on Metabolomics. Int J Anal Chem 2024; 2024:6684677. [PMID: 38204992 PMCID: PMC10776191 DOI: 10.1155/2024/6684677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/30/2023] [Accepted: 12/16/2023] [Indexed: 01/12/2024] Open
Abstract
According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.
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Affiliation(s)
- Zhang Shixiong
- Nanjing University of Chinese Medicine, Nanjing 210046, China
| | - Liu Shaowei
- Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Yang Zeqi
- Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Xu Miaochan
- Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Zhou Pingping
- Hebei Hospital of Traditional Chinese Medicine, Shijiazhuang 050091, China
| | - Bai Haiyan
- Hebei Hospital of Traditional Chinese Medicine, Shijiazhuang 050091, China
| | - Lv Jingjing
- Hebei Hospital of Traditional Chinese Medicine, Shijiazhuang 050091, China
| | - Wang Yangang
- The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing 100029, China
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14
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He S, Sun Y, Sun W, Tang M, Meng B, Liu Y, Kong Q, Li Y, Yu J, Li J. Oral microbiota disorder in GC patients revealed by 2b-RAD-M. J Transl Med 2023; 21:831. [PMID: 37980457 PMCID: PMC10656981 DOI: 10.1186/s12967-023-04599-1] [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: 06/27/2023] [Accepted: 10/06/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Microbiota alterations are linked with gastric cancer (GC). However, the relationship between the oral microbiota (especially oral fungi) and GC is not known. In this study, we aimed to apply 2b-RAD sequencing for Microbiome (2b-RAD-M) to characterize the oral microbiota in patients with GC. METHODS We performed 2b-RAD-M analysis on the saliva and tongue coating of GC patients and healthy controls. We carried out diversity, relative abundance, and composition analyses of saliva and tongue coating bacteria and fungi in the two groups. In addition, indicator analysis, the Gini index, and the mean decrease accuracy were used to identify oral fungal indicators of GC. RESULTS In this study, fungal imbalance in the saliva and tongue coating was observed in the GC group. At the species level, enriched Malassezia globosa (M. globosa) and decreased Saccharomyces cerevisiae (S. cerevisiae) were observed in saliva and tongue coating samples of the GC group. Random forest analysis indicated that M. globosa in saliva and tongue coating samples could serve as biomarkers to diagnose GC. The Gini index and mean decreases in accuracy for M. globosa in saliva and tongue coating samples were the largest. In addition, M. globosa in saliva and tongue coating samples classified GC from the control with areas under the receiver operating curve (AUCs) of 0.976 and 0.846, respectively. Further ecological analysis revealed correlations between oral bacteria and fungi. CONCLUSION For the first time, our data suggested that changes in oral fungi between GC patients and controls may help deepen our understanding of the complex spectrum of the different microbiotas involved in GC development. Although the cohort size was small, this study is the first to use 2b-RAD-M to reveal that oral M. globosa can be a fungal biomarker for detecting GC.
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Affiliation(s)
- Shengfu He
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yating Sun
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Weijie Sun
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Mingyang Tang
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Bao Meng
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yanyan Liu
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Anhui Center for Surveillance of Bacterial Resistance, Hefei, Anhui, China
- Institute of Bacterial Resistance, Anhui Medical University, Hefei, Anhui, China
| | - Qinxiang Kong
- Department of Infectious Diseases, Chaohu Hospital of Anhui MedicalUniversity, Hefei, Anhui, China
| | - Yongxiang Li
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Jiawen Yu
- Department of Oncology, Anqing First People's Hospital of Anhui Medical University/Anqing First People's Hospital of Anhui Province, Anqing, China.
| | - Jiabin Li
- Department of Infectious Diseases, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
- Anhui Center for Surveillance of Bacterial Resistance, Hefei, Anhui, China.
- Institute of Bacterial Resistance, Anhui Medical University, Hefei, Anhui, China.
- Department of Infectious Diseases, Chaohu Hospital of Anhui MedicalUniversity, Hefei, Anhui, China.
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15
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Zhang Q, Yang M, Zhang P, Wu B, Wei X, Li S. Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0129. [PMID: 37589244 PMCID: PMC11033716 DOI: 10.20892/j.issn.2095-3941.2023.0129] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/26/2023] [Indexed: 08/18/2023] Open
Abstract
Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.
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Affiliation(s)
- Qian Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Mingran Yang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Bowen Wu
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaosen Wei
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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17
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Song HJ, Park YJ, Jeong HY, Kim BG, Kim JH, Im YG. Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1293. [PMID: 37512104 PMCID: PMC10385577 DOI: 10.3390/medicina59071293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study collected 175 clinical photographic images of the dorsal tongue surface, which were divided into 7782 cropped images classified into normal, abnormal, and non-tongue regions and trained using the VGG16 deep learning model. The 80 photographic images of the entire dorsal tongue surface were used for the segmentation of abnormal regions using point mapping segmentation. Results: The F1-scores of the abnormal and normal classes were 0.960 (precision: 0.935, recall: 0.986) and 0.968 (precision: 0.987, recall: 0.950), respectively, in the prediction of the VGG16 model. As a result of evaluation using point mapping segmentation, the average F1-scores were 0.727 (precision: 0.717, recall: 0.737) and 0.645 (precision: 0.650, recall: 0.641), the average intersection of union was 0.695 and 0.590, and the average precision was 0.940 and 0.890, respectively, for abnormal and normal classes. Conclusions: The deep learning algorithm used in this study can accurately determine abnormal areas on the dorsal tongue surface, which can assist in diagnosing specific diseases or conditions of the tongue mucosa.
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Affiliation(s)
- Ho-Jun Song
- Department of Dental Materials, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Yeong-Joon Park
- Department of Dental Materials, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Hie-Yong Jeong
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Byung-Gook Kim
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Jae-Hyung Kim
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
| | - Yeong-Gwan Im
- Department of Oral Medicine, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea
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Shi Y, Guo D, Chun Y, Liu J, Liu L, Tu L, Xu J. A lung cancer risk warning model based on tongue images. Front Physiol 2023; 14:1154294. [PMID: 37324390 PMCID: PMC10267397 DOI: 10.3389/fphys.2023.1154294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Abstract
Objective: To investigate the tongue image features of patients with lung cancer and benign pulmonary nodules and to construct a lung cancer risk warning model using machine learning methods. Methods: From July 2020 to March 2022, we collected 862 participants including 263 patients with lung cancer, 292 patients with benign pulmonary nodules, and 307 healthy subjects. The TFDA-1 digital tongue diagnosis instrument was used to capture tongue images, using feature extraction technology to obtain the index of the tongue images. The statistical characteristics and correlations of the tongue index were analyzed, and six machine learning algorithms were used to build prediction models of lung cancer based on different data sets. Results: Patients with benign pulmonary nodules had different statistical characteristics and correlations of tongue image data than patients with lung cancer. Among the models based on tongue image data, the random forest prediction model performed the best, with a model accuracy of 0.679 ± 0.048 and an AUC of 0.752 ± 0.051. The accuracy for the logistic regression, decision tree, SVM, random forest, neural network, and naïve bayes models based on both the baseline and tongue image data were 0.760 ± 0.021, 0.764 ± 0.043, 0.774 ± 0.029, 0.770 ± 0.050, 0.762 ± 0.059, and 0.709 ± 0.052, respectively, while the corresponding AUCs were 0.808 ± 0.031, 0.764 ± 0.033, 0.755 ± 0.027, 0.804 ± 0.029, 0.777 ± 0.044, and 0.795 ± 0.039, respectively. Conclusion: The tongue diagnosis data under the guidance of traditional Chinese medicine diagnostic theory was useful. The performance of models built on tongue image and baseline data was superior to that of the models built using only the tongue image data or the baseline data. Adding objective tongue image data to baseline data can significantly improve the efficacy of lung cancer prediction models.
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Affiliation(s)
- Yulin Shi
- Experimental Education Center of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dandan Guo
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yi Chun
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiayi Liu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lingshuang Liu
- Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Liping Tu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiatuo Xu
- School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Satapathy P, Pradhan KB, Rustagi S, Suresh V, Al-Qaim ZH, Padhi BK, Sah R. Application of machine learning in surgery research: current uses and future directions - editorial. Int J Surg 2023; 109:1550-1551. [PMID: 37094825 PMCID: PMC10389442 DOI: 10.1097/js9.0000000000000421] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Affiliation(s)
| | - Keerti B. Pradhan
- Department of Healthcare Management, Chitkara University Punjab, Patiala
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand
| | | | - Zahraa H. Al-Qaim
- Department of Anesthesia Techniques, Al-Mustaqbal University College, Hillah, Babylon, Iraq
| | - Bijaya K. Padhi
- Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh
| | - Ranjit Sah
- Department of Clinical Microbiology, Dr. D.Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
- Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, India
- Tribhuvan University Teaching Hospital, Kathmandu, Nepal
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