Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE methodologies were implemented to determine key hub genes and assess the diagnostic prowess of nine selected FAMGs in sepsis identification. The study also investigated the correlation between these hub FAMGs. Validation was conducted through expression-level analysis using the GSE13904 and GSE65682 datasets. The study identified 13 sepsis-associated FAMGs, including ABCD2, ACSL3, ACSM1, ACSS1, ACSS2, ACOX1, ALDH9A1, ACACA, ACACB, FASN, OLAH, PPT1, and ELOVL4. As demonstrated by functional enrichment analysis results, these genes played key roles in several critical biological pathways, such as the Peroxisome, PPAR signaling pathway, and Insulin signaling pathway, all of which are intricately linked to metabolic regulation and inflammatory responses. The diagnostic potential of these FAMGs was further highlighted. In short, the expression patterns of these FAMGs c effectively distinguished sepsis cases from non-septic controls, which suggested that they may be promising biomarkers for early sepsis detection. This discovery not only enhanced our understanding of the molecular mechanisms underpinning sepsis but also paved the way for developing novel diagnostic tools and therapeutic strategies targeting metabolic dysregulation in septic patients. This research sheds light on 13 FAMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings underscore the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review

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.

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (""), 322 patterns(""), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

Validation of evidence-based questionnaire for TCM syndrome differentiation of heart failure and evaluation of expert consensus

BACKGROUND

Traditional Chinese Medicine (TCM) is widely used to treat heart failure (HF). Syndrome differentiation is a unique and crucial component in TCM practice for guiding disease diagnosis and treatment strategies as well as clinical research. The major bottlenecks in TCM syndrome differentiation are the diversity of the syndrome differentiation criteria and the broad spectrum of syndrome patterns, hindering evidence-based studies for clinical research. In the present study, we aim to develop an evidence-based questionnaire for the diagnosis of HF and establish a definitive set of criteria for syndrome differentiation.

METHODS

We designed a TCM syndrome differentiation questionnaire for heart failure (SDQHF) based on the "TCM expert consensus for diagnosis and treatment of heart failure" (expert consensus), literature review, and various clinical guidelines. To test the reliability and efficiency of the questionnaire, we performed a large-scale multiple-center clinical trial with the recruitment of 661 HF patients. Cronbach's alpha was used to assess the internal consistency of the SDQHF. Content validity was conducted through expert review. Principal component analysis (PCA) was applied to evaluate the construct validity. We constructed a proposed model for syndrome differentiation for HF based on the PCA results. Tongue analysis was performed to verify the accuracy of syndromes derived from the proposed model and the expert consensus. An evidence-based practical questionnaire for TCM syndrome differentiation patients was developed and validated with the data from 661 HF patients.

RESULTS

The syndrome differentiation criteria were constructed with five syndrome elements (qi-deficiency, yang-deficiency, yin-deficiency, blood stasis, and phlegm retention). The results revealed good convergent and discriminant validity, satisfactory internal consistency, and feasibility. The significant discoveries include: (1) A total of 91% of the derived TCM syndromes from the proposed model matched with the characterized tongue images of the syndrome patterns; (2) Qi Deficiency Syndrome is the dominant syndrome pattern for HF patients, followed by Yang-Qi Deficiency Syndrome and Qi-yin deficiency Syndrome, and finally, Yin-Yang Dual Deficiency Syndrome; (3) The majority of the HF patients had the combination of Blood Stasis and Phlegm Retention Syndromes; (4) The "Yin-Yang Dual Deficiency" Syndrome was a valid syndrome for HF, suggesting that this syndrome pattern should be included in the criteria for syndrome differentiation; and (5) Through the validation of the expert consensus, several recommendations were proposed to improve the accuracy of syndrome differentiation of HF.

CONCLUSIONS

The proposed SDQHF and the criteria could be a reliable and valid tool for syndrome differentiation of heart failure with high accuracy. It is recommended to use the proposed model for evidence-based study on Chinese Medicine to diagnose and treat HF.

TRIAL REGISTRATION NUMBER

The trial was registered at the Chinese Clinical Trial Registry, http://www.chictr.org.cn . (Registration No.: ChiCTR1900021929); Date: 2019-03-16.

MEDICLOUD: a holistic study on the digital evolution of medical data

The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status. Objective: To categorize and seek the current usage of AI in CAM. Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies. Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians. Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine

Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. Therefore, humans have been working hard to combat gastric cancer. At present, the most commonly used treatment method is radiotherapy. However, this method will damage the normal tissues of the irradiated area while treating malignant tumor cells. It not only has side effects of damage to the patient's skin and mucous membranes but also needs high-rate radiotherapy and has high cost for chemotherapy. In order to solve these problems, it is necessary to find new treatment methods. This article proposes the use of Chinese medicine to invigorate the spleen to inhibit human gastric cancer cells. This article combines modern machine learning technology with traditional Chinese medicine and combines traditional Chinese medicine physiotherapy with Western medicine nude mouse transplantation experiments. The treatment of tumors in Chinese medicine is based on the theory of Chinese medicine and has different characteristics. Western medicine has the advantage of permanently injuring patients. The process of the experiment is to transplant human-derived gastric cancer cells into nude mice. After grouping treatments and obtaining comparative data, deep learning techniques are used to analyze the properties of Chinese medicines for strengthening the spleen and to compare the properties of Chinese medicines for strengthening the spleen. The experimental results showed that the tumor inhibition rate of mice using fluorouracil was 18%, the tumor inhibition rate of mice using low-dose Chinese medicine was 16%, and the tumor inhibition rate of mice using high-dose Chinese medicine reached 52%. 80 days after the experiment, the survival rate of mice using high-dose Chinese medicine is 100% higher than that of mice without treatment.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine

As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What’s more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

Background

In pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning.

Objective

The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals.

Methods

Structured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making.

Results

The highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330.

Conclusions

Ensemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification.