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.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Although artificial intelligence(AI)was initially developed many years ago,it has experienced spectacular advances over the last 10 years for application in the field of medicine,and is now used for diagnostic,therapeutic and prognostic purposes in almost all fields.Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma(HCC),as this is a very common tumor,with particular radiological characteristics that allow its diagnosis without the need for a histological study.However,the interpretation and analysis of the resulting images is not always easy,in addition to which the images vary during the course of the disease,and prognosis and treatment response can be conditioned by multiple factors.The vast amount of data available lend themselves to study and analysis by AI in its various branches,such as deeplearning(DL)and machine learning(ML),which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation.ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns.DL is a more extensive form of learning that attempts to simulate the working of the human brain,using a lot more data and more complex algorithms.This review specifies the type of AI used by the various authors.However,welldesigned prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice.In addition,professionals now need to understand the true usefulness of these techniques,as well as their associated strengths and limitations.

Development of artificial intelligence technology in diagnosis,treatment,and prognosis of colorectal cancer

Artificial intelligence(AI)technology has made leaps and bounds since its invention.AI technology can be subdivided into many technologies such as machine learning and deep learning.The application scope and prospect of different technologies are also totally different.Currently,AI technologies play a pivotal role in the highly complex and wide-ranging medical field,such as medical image recognition,biotechnology,auxiliary diagnosis,drug research and development,and nutrition.Colorectal cancer(CRC)is a common gastrointestinal cancer that has a high mortality,posing a serious threat to human health.Many CRCs are caused by the malignant transformation of colorectal polyps.Therefore,early diagnosis and treatment are crucial to CRC prognosis.The methods of diagnosing CRC are divided into imaging diagnosis,endoscopy,and pathology diagnosis.Treatment methods are divided into endoscopic treatment,surgical treatment,and drug treatment.AI technology is in the weak era and does not have communication capabilities.Therefore,the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients.This article reviews the application of AI in the diagnosis,treatment,and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

Interpretation of diagnosis and treatment scheme of COVID-19 in Hunan province (the third trial version) based on three factors and measures

The similarities and differences between"Novel Coronavirus Infected pneumonia in Hunan province(trial second edition)"and"Novel Coronavirus Infected pneumonia(Trial seventh edition)"released by the National Health Commission and their ideas were interpreted based on"suiting measures to three factors".①In Hunan program,the third version of the trial adjustment more suitable for local characteristics of the disease;②The prevention program population classification is clear,support,eliminate pathogenic factors of the primary and secondary distinct;③The cause of disease is prominent,damp and toxin are mixed with dryness,and the treatment is mainly to moisten the lung and clear away heat,and the prescription is mostly Xinliang light agent.

Meta-analysis of the diagnosis and treatment of traditional medicine for acute coronary syndrome

The morbidity and mortality of acute coronary syndrome disease is extremely high and is the main cause of human death.As an important part of the wisdom of the Chinese nation,TCM plays an important role in treatment.By reviewing recent related research and reports on TCM theories,Chinese herbal formulas,Chinese and Western medicine treatment and other considerations,it aims to provide ideas and references for clinical treatment.

Research Status of TCM Rehabilitation in Chronic Obstructive Pulmonary Disease (COPD)

This paper mainly analyzes the application status of TCM rehabilitation in chronic obstructive pulmonary disease(COPD),hoping to provide support and help for clinical staff through this study,and promote the further development of COPD rehabilitation program.

Application of artificial intelligence in liver diseases: From diagnosis to treatment

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden.There were an estimated 2.14 million liver-related deaths in 2017,representing an 11.4%increase since 2012.Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease.As a hot research topic in recent years,the application of artificial intelligence(AI)in different fields has attracted extensive attention,and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases.Machine learning(ML)is the core of AI and the basic way to make a computer intelligent.ML technology has many potential uses in hepatology,ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis.The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly,accurately and scientifically,which is of importance for reducing the incidence and mortality of liver diseases,reducing medical errors,and promoting the development of medicine.This paper reviews the application and prospects of AI in liver diseases,and aims to improve clinicians’awareness of the importance of AI in the diagnosis and treatment of liver diseases.

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma:Current status and future perspectives

Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.Artificial intelligence(AI),such as machine learning(ML)and deep learning,has recently gained attention for its capability to reveal quantitative information on images.Currently,AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine.This review summarizes the applications of AI in various aspects of preoperative imaging of HCC,including segmentation,differential diagnosis,prediction of histo-pathology,early detection of recurrence after curative treatment,and evaluation of treatment response.We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.

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.

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.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Over the past decade,artificial intelligence(AI)has contributed substantially to the resolution of various medical problems,including cancer.Deep learning(DL),a subfield of AI,is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data.On the basis of a large quantity of medical data and novel computational technologies,AI,especially DL,has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment.These applications range from early cancer detection,diagnosis,classification and grading,molecular characterization of tumors,prediction of patient outcomes and treatment responses,personalized treatment,automatic radiotherapy workflows,novel anti-cancer drug discovery,and clinical trials.In this review,we introduced the general principle of AI,summarized major areas of its application for cancer diagnosis and treatment,and discussed its future directions and remaining challenges.As the adoption of AI in clinical use is increasing,we anticipate the arrival of AI-powered cancer care.

Development of community health service-oriented computer-assisted information system for diagnosis and treatment of respiratory diseases

Objective:Community health services are an emerging trend.We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct.The respiratory department’s daily work involves a number of outpatient registration items and a vast workload.The routine manual operation is inefficient and it is not convenient to make effective statistical analysis of the outpatient data to identify the risk factors closely related to diseases.Therefore,it is imperative to process the outpatient information of patients with respiratory diseases effectively and efficiently in a unified manner by means of computer technology.Methods:The design and realization of the Community Health Service-oriented computerassisted Information System for Diagnosis and Treatment of Respiratory Diseases(CHS-DTRD)was completed as part of the community intervention study on bronchial asthma that was carried out jointly by the Nanjing First Hospital Affiliated to Nanjing Medical University and the Hospital of Nanjing University of Science&Technology,and based on 2 years of experience and the needs of an overall analysis.Results:The computer-assisted information system for diagnosis and treatment was developed using Java Server Page(JSP)technology and introducing the advanced Asynchronous JavaScript XML(AJAX)technique and MS-SQL Server was used in the background database.CHS-DTRD was composed of eight functional modules(outpatient data maintenance,outpatient appointment,intelligent analysis for disease risk factors,query and statistics,data dictionary maintenance,database manipulation,access control,and system configuration).CHS-DTRD featured a friendly interface,convenient operation,and stability and reliability.Conclusion:Community health-oriented diagnosis and treatment of respiratory diseases is simple,programmable,and intuitive,thus the workload of physicians is significantly reduced and the work efficiency is improved.This system facilitates an intelligent analysis of disease risk factors using data mining technology,and provides physicians with suggestions on intelligent analysis for diagnosis of disease and conclusion of disease causes.

Artificial intelligence and cholangiocarcinoma:Updates and prospects

Artificial intelligence(AI)is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems.In the era of“Big data”,there is an ever-increasing need for AI in all aspects of medicine.Cholangiocarcinoma(CCA)is the second most common primary malignancy of liver that has shown an increase in incidence in the last years.CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery,chemotherapy,and other modalities.With technological advancement there is an immense amount of clinicopathologic,genetic,serologic,histologic,and radiologic data that can be assimilated together by modern AI tools for diagnosis,treatment,and prognosis of CCA.The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis,treatment,and prognosis of CCA.Most studies however are retrospective,and one study failed to show AI benefit in practice.There is immense potential for AI in diagnosis,treatment,and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.

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.

Application of artificial intelligence in clinical diagnosis and treatment:an overview of systematic reviews

Objective This study aimed to summarize the characteristics and methodological quality of systematic reviews on the application of artificial intelligence(AI)in clinical diagnosis and treatment.Methods We systematically searched seven English-and Chinese-language literature databases to identify sys-tematic reviews on the application of AI,deep learning,or machine learning in the diagnosis and treatment of any disease published in 2020.We evaluated the methodological quality of the included systematic reviews using“A Measurement tool for the assessment of multiple systematic reviews”(AMSTAR).We also conducted meta-analyses on the diagnostic accuracy of AI on selected disease categories with a large number of included studies and low clinical heterogeneity.Results A total of 40 systematic reviews reporting 1,083 original studies were included,covering 31 diseases from 11 groups of diseases.Eleven systematic reviews were related to neoplasms and nine were systematic reviews related to diseases of the digestive system.We selected digestive system diseases for the meta-analysis.The pooled sensitivities(with 95%confidence interval(CI))of AI to assist the diagnosis of helicobacter pylori,gastrointestinal ulcers,hemorrhage,esophageal tumors,gastric tumors,and intestinal tumors(with 95%CI)were 0.91(0.83-0.95),0.99(0.76-1.00),0.95(0.83-0.99),0.90(0.85-0.93),0.90(0.82-0.95),and 0.93(0.88-0.96),respectively,and the pooled specificities were 0.82(0.77-0.87),0.97(0.86-1.00),1.00(0.99-1.00),0.80(0.71-0.87),0.93(0.87-0.97),and 0.89(0.85-0.92),respectively.The AMSTAR items“the list of included studies”(n=39,97.5%)and“the characteristics of the included studies”(n=39,97.5%)had the highest compliance among the reviews;the compliance was relatively low to the items“the consideration of publication status”(n=1,2.5%),“the consideration of scientific quality”(n=19,47.5%),“data synthesis methods”(n=18,45.0%),and“the evaluation of publication bias”(n=13,32.5%).Conclusions The main subjects of systematic reviews on AI applications in clinical diagnosis and treatment pub-lished in 2020 were diseases of the digestive system and neoplasms.The methodological quality of the systematic reviews on AI needs to be improved,paying particular attention to publication bias and the rigorous evaluation of the quality of the included studies.

中医智能推理辨证模型探索

虽然中医人工智能辨证诊断技术不断发展,但现有的智能辨证模型只能输出辨证诊断结果,无法展现辨证推理过程,不具有中医诊断学知识的可解释性,难以真正体现中医的辨证思维过程,这可能是困惑已久的难题。本文阐述了中医智能辨证诊断的发展历程与技术特点,探讨了当前中医智能辨证诊断研究在中医辨证思维推理解析问题上遇到的关键技术及瓶颈,提出了一种可推理解释中医辨证思维过程的新型智能辨证诊断模型架构,并阐述了模型与算法的实现过程,以及进行了详细的数据实例分析。本文将为中医的智能辨证发展提供了新的方法,为中医临床辅助决策打开了新的研究思路。

Artificial intelligence and colorectal cancer:How far can you go?

Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields.The numerous applications of artificial intelligence in gastroenterology have shown promising results,especially in the setting of gastrointestinal oncology.Therefore,we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis,treatment,and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.

女性盆底功能障碍性疾病诊治流程及物理治疗康复临床应用——康复流程、原则及智能诊疗

女性盆底功能障碍性疾病(FPFD)发生机制复杂,专科的临床医师往往无法覆盖妇产、泌尿、肛肠、影像等各专科全面的医学专业信息,限制了对于该疾病的系统、全面诊治。FPFD的治疗需要遵循康复流程,制定个性化物理治疗干预措施,同时结合其他方式制定综合性治疗策略。人工智能在盆底领域的应用,不仅可以提高医生诊断效率,还可以为综合治疗方案的制定提供参考和依据,实现智能诊疗与精准医疗。本文从FPFD康复流程出发,分析康复原则及内容,探究人工智能在FPFD诊疗中的应用现状,旨在为实现盆底疾病智能诊疗提供参考。