Personalized Body Constitution Inquiry Based on Machine Learning

Chaos-MLP: Chaotic Transform MLP-Like Architecture for Medical Images Multi-Label Recognition Task

The theory of "three-stage prevention" in view of the body constitution is the key technology of modern Chinese medicine for "Preventive Treatment of Diseases". In particular, automated body constitution recognition (BCR) is an integral part of intelligent Traditional Chinese Medicine (TCM), which is extremely valuable for disease prevention and diagnosis. Actually, BCR is a challenging multi-label recognition task by the TCM composite constitution theory. First, two new databases are constructed, one is a multi-label facial body constitution (MFBC), and another is a multi-label tongue body constitution (MTBC). Second, a novel MLP-like architecture, named Chaos-MLP, is designed for the BCR task, which interacts with the channel chaotic features of extracted medical images and fuses them with the width and height channel direction features, respectively. Notably, the chaotic transform can enhance the distinguishability of extracted features from the medical images. Moreover, we propose a binary center cognitive gravity loss (BCCGL) to enhance the learning ability of the Chaos-MLP for unbalanced body constitution labels. Our proposed method shows superior performance on both MFBC and MTBC datasets than other state-of-the-art (SOTA) MLP-like networks and a vision graph-based neural network (VGNN), which include Wave-MLP, Cycle-MLP, Vip, and Active-MLP.

Advancements in Artificial Intelligence-Driven Diagnostic Models for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is an ancient medical system with distinctive ethnic characteristics. TCM diagnosis, underpinned by unique theoretical frameworks and methodologies, continues to play a significant role in contemporary healthcare. The four fundamental diagnostic methods, inspection, auscultation-olfaction, inquiry and palpation, are inherently subjective, relying on practitioner experience. Despite its unique advantages and practical value, TCM must still take advantage of modern advancements to enhance its effectiveness and accessibility. With the rapid development of computer technology, intelligent TCM diagnosis has emerged as a promising frontier. Integrating artificial intelligence (AI), particularly through large language models (LLMs), offers new avenues for enhancing TCM diagnostic practices. However, the systematic review and analysis of these technologies remains limited. This paper provides a comprehensive overview of the development and recent advancements in TCM diagnostic technologies, focusing on the applications of ML across various data modalities, and including images, text, and waveforms. Additionally, it explores the latest applications of LLMs within the TCM diagnostic field. Furthermore, the review discusses the prospects and challenges associated with AI-based TCM diagnosis. By systematically summarizing the latest research achievements and technological advancements, this study aims to provide directional guidance and decision support for future research and practical applications in the intersection of AI and TCM. Ultimately, this review seeks to foster the continued development and integration of intelligent TCM diagnosis into modern healthcare.

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.

The Holistic Philosophy of Traditional Chinese Medicine and Conflicts With Modern Medicine

Traditional Chinese medicine (TCM) has sparked the public's attention for its potential in new drug development and its holistic view toward health, which is totally different from the reductionistic science of modern medicine. Although many scholars try to connect TCM with precision medicine or apply new methods and technology to integrate TCM with modern medicine, the misunderstandings and gap between TCM and modern medicine limit the development of evidence-based TCM. Traditional Chinese medicine is actually a medical science encompassing not only medicine but also philosophy and art in direct contrast to molecular-based modern medicine. As more and more multidisciplinary studies are being published, finding ways to integrate TCM with modern or precision medicine through artificial intelligence, new study design and technology may become a critical issue. This article aims to briefly review the unique philosophy of TCM and its conflicts with modern medicine, with a focus on the potential integration of TCM and modern medicine. We also provide insight for the key attributes of TCM and the associated investigation with Western research approaches.

Traditional Chinese Medicine constitution types of high-normal blood pressure: A meta-analysis

Objective

We determined the distribution of constitutional types of high-normal blood pressure in Traditional Chinese Medicine (TCM) and provided evidence for the prevention of high-normal blood pressure and hypertension.

Methods

Eight digital databases were searched from January 2011 to November 2022, including PubMed, EMBASE, Web of Science, EBSCOhost, CNKI, CBM, Wangfang, and CQVIP. We performed a meta-analysis with the random-effects model or fixed-effects model to describe the distribution of constitutional types of high-normal blood pressure in TCM. The studies were assessed based on heterogeneity testing and the potential for publication bias. The meta-analysis was performed on Stata software 15.0.

Results

A total of 17 studies with 8118 participants were included in this meta-analysis. The proportion of the biased constitution (82.3%; 95% CI: 75.6%-89.1%, p < 0.001) was higher than the balanced constitution (17.3%; 95% CI: 10.7-23.8%, p < 0.001). Phlegm-dampness constitution, Yin-deficiency constitution, and damp-heat constitution accounted for 16.0% (95%CI: 10.5-21.5%, p < 0.001), 14.8% (95% CI: 11.0-18.6%, p < 0.001), 11.3% (95% CI: 8.0-14.5%, p < 0.001) of the total high-normal blood pressure cases, respectively. The subgroup analyses performed that region, age and gender were positively associated with the distribution of constitution types of high-normal blood pressure in TCM. Compared with the general population, the risk of high-normal blood pressure in people with the phlegm-dampness constitution, Yin-deficiency constitution, and blood-stasis constitution was 2.665 (95%CI: 2.286-3.106, p < 0.001), 2.378 (95%CI: 1.197-4.724, p = 0.013), 1.965 (95%CI: 1.634-2.363, p < 0.001) times of the general population, respectively. Meanwhile, the risk of high-normal blood pressure was lower in people with a balanced constitution (0.248, 95%CI: 0.165-0.372, p < 0.001).

Conclusions

Phlegm-dampness constitution, Yin-deficiency constitution, and damp-heat constitution were the common constitution types of high-normal blood pressure. There might also be differences in the distribution characteristics of TCM constitution among people with high-normal blood pressure in different regions, ages, and genders. Finally, a balanced constitution might be a protective factor for hypertensive people.

Traditional Chinese Medicine Constitution Identification Based on Objective Facial and Tongue Features: A Delphi Study and a Diagnostic Nomogram for Blood Stasis Constitution

Objective

The aim of this study was to systematically summarize and form an expert consensus on the theoretical experience of tongue and facial features for the identification of nine types of traditional Chinese medicine (TCM) constitution. Additionally, we sought to explore the feasibility of TCM constitution identification through objective tongue and facial features.

Methods

We used Delphi method to investigate the opinions of experts on facial and tongue feature items for identifying TCM constitution. We developed and validated a diagnostic nomogram for blood stasis constitution (BSC) based on objective facial and tongue features to demonstrate the reliability of expert consultation.

Results

Eleven experts participated in two rounds of expert consultation. The recovery rates of the two rounds of expert consultation were 100.0% and 90.9%. After the first round, 39 items were screened out from 147 initial items, and 2 items were supplemented by experts. In the second round, 7 items were eliminated, leaving 34 items for 8 types of TCM constitution. The coefficient of variation in the first round was 0.11–0.49 for the 147 items and 0.11–0.29 for the included items. The coefficient of variation in the second round was 0.10–0.27 for the 41 items and 0.10–0.20 for the included items. The W value was 0.548 (P < 0.001) in the first round and 0.240 (P < 0.001) in the second round. Based on expert consultation, we selected BSC as an example and developed and validated a diagnostic nomogram consisting of six indicators: sex, hair volume, lip color-dark purple, susceptibility-facial pigmentation/chloasma/ecchymosis, zygomatic texture-red blood streaks, and sublingual vein-varicose and dark purple. The nomogram showed good discrimination (AUC: 0.917 [95% confidence interval [CI], 0.877–0.956] for the primary dataset, 0.902 [95% CI, 0.828–0.976] for the validation dataset) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion

This is the first study to systematically summarize the existing knowledge and clinical experience to form an expert consensus on the tongue and facial features of nine types of TCM constitution. Our results will provide important prior knowledge and expert experience for future constitution identification research. Based on expert consultation, this study presents a nomogram for BSC that incorporates objective facial and tongue features, which can be conveniently used to facilitate the individualized identification of BSC.

Perceptions of traditional Chinese medicine doctors about using wearable devices and traditional Chinese medicine diagnostic instruments: A mixed-methodology study

Objective

This study aimed to investigate the perceptions of traditional Chinese medicine doctors about wearable devices and diagnostic instruments and explore the factors that influence them.

Methods

Data on the perceptions of the traditional Chinese medicine doctors in Hangzhou, China, about wearable devices and diagnostic instruments were collected through face-to-face semi-structured interviews. The author coded the interview responses using grounded theory. A cross-sectional survey was conducted in four traditional Chinese medicine hospitals in Hangzhou, China. The responses of 385 traditional Chinese medicine doctors were considered valid. Descriptive statistics and binary logistic regression models were used for analysis.

Results

This study categorized the perceptions of traditional Chinese medicine about wearable devices and traditional Chinese medicine diagnostic instruments under convenience, reliability, suitable population, machine usage scenario, and the integration of traditional Chinese medicine and information communication technology. Convenience encompassed portability and the convenience of carrying instruments or wearing the devices and operating them and the human-device interface. Reliability encompassed the underlying principles, accuracy, durability, and reference to diagnosis. Suitability for people encompassed age distinction and disease differentiation. Machine usage scenarios included use in daily life, educational institutions, and primary medical institutions. The combination of traditional Chinese medicine and information communication technology encompassed the integration of traditional Chinese medicine and wearable functions and diagnostic interpretation. The perceptions of traditional Chinese medicine doctors were affected by age, title, type of hospital, and specialty.

Conclusions

The use of wearable devices and traditional Chinese medicine diagnostic instruments has gradually been accepted by traditional Chinese medicine doctors. Traditional Chinese medicine doctors need to improve their knowledge and skills for information communication technology integration, and their standardized training should incorporate information communication technology and digital health.

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.