A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Digital intelligence technology: new quality productivity for precision traditional Chinese medicine

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.

NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection

Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality of life. They cause nasal congestion, facial pain, headaches, thick nasal discharge, and a reduced sense of smell. However, accurately diagnosing these diseases is challenging due to multiple factors, including inadequate patient adherence to pre-diagnostic protocols. By leveraging the latest developments in Artificial Intelligence (AI), there exists a substantial opportunity to improve the precision and effectiveness of classification of these diseases. In this study, we present a novel AI-based approach for sinonasal pathology detection, using Self-Supervised Learning (SSL) techniques and Random Forest (RF) algorithms. We have collected a new diagnostic imaging dataset, which is a major contribution to this study. The dataset contains 137 CT and MRI images meticulously labeled by expert radiologists, with two classes: healthy and unhealthy (sinus disease). This dataset is a useful asset for developing and evaluating AI-based classification techniques. In addition, our proposed approach employs the Deep InfoMax (DIM) model to extract meaningful global and local features from the imaging data with a self-supervised method. These features are then used as input for an RF classifier, which effectively distinguishes between healthy and sinonasal pathological cases. The combination of both DIM and RF provides efficient feature learning and powerful classification of sinus cases. Our preliminary results demonstrate the efficiency of the proposed approach, which achieves a mean classification accuracy of 92.62%. These findings highlight the potential of our AI-based approach in improving sinonasal pathology diagnosis.

Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions

BACKGROUND

The individualized prediction and discrimination of precancerous lesions of gastric cancer (PLGC) is critical for the early prevention of gastric cancer (GC). However, accurate non-invasive methods for distinguishing between PLGC and GC are currently lacking. This study therefore aimed to develop a risk prediction model by machine learning and deep learning techniques to aid the early diagnosis of GC.

METHODS

In this study, a total of 2229 subjects were recruited from nine tertiary hospitals between October 2022 and November 2023. We designed a comprehensive questionnaire, identified statistically significant factors, and created a web-based column chart. Then, a risk prediction model was subsequently developed by machine learning techniques. In addition, a tongue image-based risk prediction model was established by deep learning algorithms.

RESULTS

Based on logistic regression analysis, a dynamic web-based nomogram was developed and it is freely accessible at: https://yz6677.shinyapps.io/GC67/ . Then, the prediction model was established using ten different machine learning algorithms and the Random Forest (RF) model achieved the highest accuracy at 85.65%. According with the predictive results, the top 10 key risk factors were age, traditional Chinese medicine (TCM) constitution type, tongue coating color, tongue color, irregular meals, pickled food, greasy fur, over-hot eating habit, anxiety and sleep onset latency. These factors are all significant risk indicators for the progression of PLGC patients to GC patients. Subsequently, the Swin Transformer architecture was used to develop a tongue image-based model for predicting the risk for progression of PLGC. The verification set showed an accuracy of 73.33% and an area under curve (AUC) greater than 0.8 across all models.

CONCLUSIONS

Our study developed machine learning and deep learning-based models for predicting the risk for progression of PLGC to GC, which will offer the assistance to determine the high-risk patients from PLGC and improve the early diagnosis of GC.

AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine

The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals.

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers' performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Background

With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment.

Methods

We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics.

Results

The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes.

Conclusions

The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.