“智医助理”在基层医疗服务的应用与探索——以安徽省试点地区寿县的实践为例

目的以“智医助理”平台为载体,构建基层医生诊疗服务新模式,提高基层医疗机构服务能力,有效提升基层医疗信息化水平和应用实效。方法通过“智医助理”辅助诊断子系统、慢病智能管理子系统、远程会诊系统和PAD移动终端,将医疗服务线上线下资源有效整合,提升基层医疗水平。结果建立全新的人机耦合基层辅助诊疗服务模式,为医生诊疗赋能、为基本公卫管理减负、为居民慢病管控服务提质。结论“智医助理”在基层的应用,推进了基层医疗服务水平的提升,改善了群众的就医感受。

“智医”世纪,何去何从?

不妨从“医”或“技”的纠结中跳脱出来,问问“健康”。就在一年前,医院门诊引入导诊机器人还是大新闻,各类新闻媒体描述标题从“有望”到“试点”到“首个”等不一,报道中,常见的患者与机器人互动画面,流露出满满的人类对这一技术生物的期待和兴奋。但近一年,这样的新闻虽然增多了,却不再特别令人兴奋。去医院,甚至会感觉.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

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.

基于智慧医疗系统提升基层卫生能力的实践与探索

近年来,我国持续加强基层医疗卫生基础设施建设,稳步提升基层医疗卫生水平,但仍然存在部分问题等待解决。伴随着科学技术的不断发展,互联网与医疗技术的结合已为大势所趋。在“互联网+医疗”等政策的背景下,文章聚焦基层医疗卫生问题,利用人工智能及云计算等技术,构建智慧医疗系统,实现基层卫生能力提升,优质医疗资源下沉,促进基层医疗卫生机构可持续发展。

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

Artificial intelligence(AI) aims to mimic human cognitive functions and execute intellectual activities like that performed by humans dealing with an uncertain environment. The rapid development of AI technology provides powerful tools to analyze massive amounts of data, facilitating physicians to make better clinical decisions or even replace human judgment in healthcare.Advanced AI technology also creates novel opportunities for exploring the scientific basis of traditional Chinese medicine(TCM) and developing the standardization and digitization of TCM pulse diagnostic methodology. In the present study, we review and discuss the potential application of AI technology in TCM pulse diagnosis. The major contents include the following aspects:(1) a brief introduction of the general concepts and knowledge of TCM pulse diagnosis or palpation,(2) landmark developments in AI technology and the applications of common AI deep learning algorithms in medical practice,(3) the current progress of AI technology in TCM pulse diagnosis,(4) challenges and perspectives of AI technology in TCM pulse diagnosis. In conclusion, the pairing of TCM with modern AI technology will bring novel insights into understanding the scientific principles underlying TCM pulse diagnosis and creating opportunities for the development of AI deep learning technology for the standardization and digitalization of TCM pulse diagnosis.

Clinical Intelligent Diagnosis Path Based on the Chief Complaint

Goals of traditional Chinese medicine(TCM)include precision,accuracy,and recognition by clinical practice.Establishment of a diagnosis and treatment system that closely conforms to the principle-method-recipe-medicines system and derivation of an accurate diagnosis and treatment plan should be considerations of TCM.Artificial intelligence research based on computer technology is one of the effective ways to solve this problem.In the research of intelligent diagnosis path,reflecting the characteristics of the overall view and dialectical treatment of TCM such as"Combination of four diagnostic methods""overall examination""combination of disease and syndrome"and"treatment individualized to patient,season and locality"are key for successful research of artificial intelligence in TCM diagnosis or recognition by clinical practice.

A smartphone-based calibration-free portable urinalysis device

As one of the most common medical diagnosis methods, urinalysis is a highly demanded technique for screening tests or daily monitoring of various diseases. With the rapid development of POC(point-of-care) systems, a convenient house-using urinalysis device is widely needed. However, considering the difference of onboard systems and multiple test indicators in urinalysis, the design of such an intelligent device is still challenging. In this paper, a smartphone-based portable urinalysis system has been developed and applied for the colorimetric analysis of routine urine examination indices using an Android app. By integrating the test paper sensor in the portable device for urinalysis,our system significantly improves the instability of conventional dipstick-based manual colorimetry, and the smartphone application used for color discrimination enhances the accuracy of the visual assessment of sample strips. Using a simple operation approach that takes ~ 2 min per test, our system can be applied as rapid urinalysis for routine check-ups.

Optimization of GERD Therapeutic Regimen Based on ANN and Realization of MATLAB

Objective To optimize therapeutic regimens for gastro-esophageal reflux disease(GERD),artificial neural networks(ANNs)are used to simulate and set up an intelligent traditional Chinese medicine(TCM)treatment system.Methods ANNs were employed for machine learning;the clinical syndrome differentiation and treatment determination were simulated through systematic learning of therapeutic regimens for GERD symptoms in the ancient literature;and case simulation was conducted to achieve objective verification.Results The conformity of machinery prescription with the ancient literature exceeded95%.Conclusion The application of machine learning to TCM intelligent prescription is feasible and worthy of further study.

Research on ＂intelligent＂ old-age Road

The wisdom of the aged has become a direction that can＇t be ignored in the development of the old-age industry. It can be combined with traditional home care, institutional pension and community pension, and can improve the efficiency of these old-age models, and can also connect the transformation of old-age service and medical service to the combination of medical support, and there are many advantages. This article will analyze the realization basis of intelligent endowment, the advantages and disadvantages of intelligent endowment, and explore how to effectively promote the development of intelligent pension industry.

Research on Text Mining of Syndrome Element Syndrome Differentiation by Natural Language Processing

Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.

Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

Cardiovascular diseases(CVDs)are major disease burdens with high mortality worldwide.Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs.The pathological mechanisms and multiple factors involved in CVDs are complex;thus,traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks,medical image interpretations,therapeutic decision-making,and disease prognosis prediction.Meanwhile,traditional Chinese medicine(TCM)has been widely used for treating CVDs.TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs.Big data have been generated to investigate the scientific basis of TCM diagnostic methods.TCM formulae contain multiple herbal items.Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability.Recent progress in artificial intelligence(AI)technology has allowed these challenges to be resolved,which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae.Herein,we briefly introduce the basic concept and current progress of AI and machine learning(ML)technology,and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs.Furthermore,we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs.We expect the application of AI and ML technology to promote synergy between western medicine and TCM,which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.

A Study on Smart Home Design Incorporating Ubiquitous Healthcare Services

The smart home using ubiquitous technology can effectively provide services to the elderly and the physically impaired. However, such services are accompanied by high initial cost of installation and the operating inefficiency due to the absence of design guidelines. To solve these, the integrated management of the process by supplying the public service like voucher schemes to the dwelling is needed. This paper mainly proposes a research about the healthcare service in a residential environment, which includes medical service and safety service and so on, by surveying the voucher program and the in-house infra status. Finally, the house planning elements for healthcare-based smart home are drawn and the planning directions through expert survey are suggested. Therefore, this study surveyed on voucher program and in-house infra status, and drew the house planning elements for health-based care smart home. In addition, this study suggested the planning direction through expert survey. This study can be used as a guideline for constructing a smart home, which supplies healthcare service.

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.

Establishing and validating a spotted tongue recognition and extraction model based on multiscale convolutional neural network

Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.

Iontronic pressure sensor with high sensitivity and linear response over a wide pressure range based on soft micropillared electrodes

Electronic skins and flexible pressure sensors are important devices for advanced healthcare and intelligent robotics.Sensitivity is a key parameter of flexible pressure sensors.Whereas introducing surface microstructures in a capacitive-type sensor can significantly improve its sensitivity,the signal becomes nonlinear and the pressure response range gets much narrower,significantly limiting the applications of flexible pressure sensors.Here,we designed a pressure sensor that utilizes a nanoscale iontronic interface of an ionic gel layer and a micropillared electrode,for highly linear capacitance-to-pressure response and high sensitivity over a wide pressure range.The micropillars undergo three stages of deformation upon loading:initial contact(0-6 k Pa)and structure buckling(6-12 k Pa)that exhibit a low and nonlinear response,as well as a post-buckling stage that has a high signal linearity with high sensitivity(33.16 k Pa-1)over a broad pressure range of 12-176 k Pa.The high linearity lies in the subtle balance between the structure compression and mechanical matching of the two materials at the gel-electrode interface.Our sensor has been applied in pulse detection,plantar pressure mapping,and grasp task of an artificial limb.This work provides a physical insight in achieving linear response through the design of appropriate microstructures and selection of materials with suitable modulus in flexible pressure sensors,which are potentially useful in intelligent robots and health monitoring.

Construction of smart inorganic nanoparticle-based ultrasound contrast agents and their biomedical applications

Ultrasound(US) imaging in combination with US contrast agents(UCAs) is a powerful tool in the modern biomedical field because of its high spatial resolution, easy access to patients and minimum invasiveness.The microbubble-based UCAs have been widely used in clinical diagnosis; however, they are only limited to the blood pool imaging and not applicable to the tissue-penetrated imaging due to their large particle size and structural instability. Inorganic nanoparticles(NPs), such as silica,gold and Fe x O y, featured with both satisfactory echogenic properties and structural stability have the potential to be used as a new generation of UCAs. In this review, we present the most recent progresses in the tailored construction of inorganic UCAs and their biomedical applications in the US imaging-involved fields. Firstly, the typical inorganic NPs with different structures including solid, hollow and multiple-layer forms will be comprehensively introduced in terms of their structure design,physicochemical property, US imaging mechanism and diverse applications; secondly, the recent progress in exploring the gas-generating inorganic NP system for US imaging purpose will be reviewed, and these intelligent UCAs are multifunctional for simultaneous US imaging and disease therapy; thirdly, several nanocomposite platforms newly constructed by combining inorganic UCAs with other functional components will be presented anddiscussed. These multifunctional NPs are capable of further enhancing the imaging resolution by providing more comprehensive anatomical information simultaneously.Last but not the least, the design criteria for developing promising UCAs to satisfy both clinical demands and optimized US imaging capability will be discussed and summarized in this review.