The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Review of eye diagnosis in traditional Chinese medicine:Modernization and future prospects

This article reviews the basic theories, methods, and clinical applications of eye diagnosis in traditional Chinese medicine(TCM). It introduces cutting-edge methods and applications and explains that the modernization of TCM eye diagnosis includes “equipment-assisted diagnosis” and “artificial intelligencebased diagnosis”. The article also notes that while there are many recent studies of the static attributes of eyes in modern TCM eye diagnosis, modern application research on the dynamic attributes of eyes in TCM diagnosis theory is relatively rare. We propose, therefore, that introducing advanced eye-movement detection technology into TCM clinical diagnosis could help to further modernize TCM eye diagnosis.

Original askiatic imaging used in Chinese medicine eye-feature diagnosis of visceral diseases

Eye-feature diagnosis is a time-homored met hod for studying many diseases in tradit ional Chinese medicine.There is a dlose relationship between eye feature and viscera,and eye feature is a reflect ion of viscer al health status.Commercially used ophthalmology diagnosis instr uments have disadvantages and cannot satisfy the requirements of eye feature diagnosis.In this paper,we proposed a novel askiatic imaging method that removes the interference of an ilumination source's reflection shadow and is free from image splicing.We developed a novel imaging system to implement this method,and some eye feature characteristics to analyze visceral diseases were obtained.

Machine learning models for stroke detection by observing the eye-movement features under five-color visual stimuli in traditional Chinese medicine

Objective:To develop a novel diagnostic modality to identify and diagnose stroke in daily life scenarios for improving the therapeutic effects and prognoses of patients.Methods:In this study,16 stroke patients and 24 age-matched healthy participants as controls were recruited for comparative analysis.Leveraging a portable eye-tracking device and integrating traditional Chinese medicine theory with modern color psychology principles,we recorded the eye movement signals and calculated eye movement features.Meanwhile,the stroke recognition models based on eye movement features were further trained by using random forest(RF),k-nearest neighbors(KNN),decision tree(DT),gradient boosting classifier(GBC),XGBoost,and CatBoost.Results:The stroke group and the healthy group showed significant differences in some eye movement features(P<.05).The models trained based on eye movement characteristics had good performances in recognizing stroke individuals,with accuracies ranging from 77.40%to 88.45%.Under the red stimulus,the eye movement model trained by RF became the best machine learning model with a recall of 84.65%,a precision of 86.48%,a F1 score of 85.47%.Among the six algorithms,RF and CatBoost performed better in classification.Conclusion:This study pioneers the application of traditional Chinese medicine's five-color stimuli to visual observation tasks.On the basis of the combined design,the eye-movement models can accurately identify stroke,and the developed high-performance models may be used in daily life scenarios.