Research hotspots and trends in transcranial magnetic stimulation for cognitive impairment:A bibliometric analysis from 2014 to 2023

BACKGROUND Cognitive impairment,which manifests as a limited deterioration of specific functions associated with a particular disease,can lead to a general deterioration of the patient’s standard of living.Transcranial magnetic stimulation,a noninvasive neuromodulation technique,is frequently employed to treat cognitive impairment in neuropsychiatric disorders.AIM To analyzed the state of international research on neuromodulation methods for treating cognitive impairment between 2014 and 2023,with the aim of exploring the state of research worldwide and the most recent developments in this particular area.METHODS Articles and reviews pertaining to neuromodulation methods for cognitive impairment were examined using the web of science database between January 2014 and December 2023.Publications,nations,organizations,writers,journals,citations,and keywords data from the identified studies were systematically analyzed using the CiteSpace 6.3.R1 software.RESULTS A total of 2371 documents with 11750 authors and 9461 institutions,with some cooccurrences,were retrieved.The quantity of yearly publications is showing an increasing trend.The United States and China have emerged as important contributors.Among the institutes,Harvard University had the highest number of publications,while Rossi S an author who is frequently cited.Initially,the primary keywords included human motor cortex,placebo-controlled trials,and serotonin reuptake inhibitors.However,the emphasis gradually moved to substance use disorders,supplementary motor areas,neural mechanisms,and exercise.CONCLUSION The use of neuromodulation techniques to treat cognitive impairment has drawn interest from academics all around the world.This study revealed hotspots and new trends in the research of transcranial magnetic stimulation as a cognitive impairment rehabilitation treatment.These findings are hold significant potential to guide further research and thus promote transcranial magnetic stimulation as a treatment method for cognitive impairment.

Prevalence of visual impairment and estimation of refractive errors among school children in Kakamega,Kenya

AIM:To investigate the prevalence of visual impairment(VI)and provide an estimation of uncorrected refractive errors in school-aged children,conducted by optometry students as a community service.METHODS:The study was cross-sectional.Totally 3343 participants were included in the study.The initial examination involved assessing the uncorrected distance visual acuity(UDVA)and visual acuity(VA)while using a+2.00 D lens.The inclusion criteria for a subsequent comprehensive cycloplegic eye examination,performed by an optometrist,were as follows:a UDVA<0.6 decimal(0.20 logMAR)and/or a VA with+2.00 D≥0.8 decimal(0.96 logMAR).RESULTS:The sample had a mean age of 10.92±2.13y(range 4 to 17y),and 51.3%of the children were female(n=1715).The majority of the children(89.7%)fell within the age range of 8 to 14y.Among the ethnic groups,the highest representation was from the Luhya group(60.6%)followed by Luo(20.4%).Mean logMAR UDVA choosing the best eye for each student was 0.29±0.17(range 1.70 to 0.22).Out of the total,246 participants(7.4%)had a full eye examination.The estimated prevalence of myopia(defined as spherical equivalent≤-0.5 D)was found to be 1.45%of the total sample.While around 0.18%of the total sample had hyperopia value exceeding+1.75 D.Refractive astigmatism(cil<-0.75 D)was found in 0.21%(7/3343)of the children.The VI prevalence was 1.26%of the total sample.Among our cases of VI,76.2%could be attributed to uncorrected refractive error.Amblyopia was detected in 0.66%(22/3343)of the screened children.There was no statistically significant correlation observed between age or gender and refractive values.CONCLUSION:The primary cause of VI is determined to be uncorrected refractive errors,with myopia being the most prevalent refractive error observed.These findings underscore the significance of early identification and correction of refractive errors in school-aged children as a means to alleviate the impact of VI.

The cardiovascular system at high altitude:A bibliometric and visualization analysis

BACKGROUND When exposed to high-altitude environments,the cardiovascular system undergoes various changes,the performance and mechanisms of which remain controversial.AIM To summarize the latest research advancements and hot research points in the cardiovascular system at high altitude by conducting a bibliometric and visualization analysis.METHODS The literature was systematically retrieved and filtered using the Web of Science Core Collection of Science Citation Index Expanded.A visualization analysis of the identified publications was conducted employing CiteSpace and VOSviewer.RESULTS A total of 1674 publications were included in the study,with an observed annual increase in the number of publications spanning from 1990 to 2022.The United States of America emerged as the predominant contributor,while Universidad Peruana Cayetano Heredia stood out as the institution with the highest publication output.Notably,Jean-Paul Richalet demonstrated the highest productivity among researchers focusing on the cardiovascular system at high altitude.Furthermore,Peter Bärtsch emerged as the author with the highest number of cited articles.Keyword analysis identified hypoxia,exercise,acclimatization,acute and chronic mountain sickness,pulmonary hypertension,metabolism,and echocardiography as the primary research hot research points and emerging directions in the study of the cardiovascular system at high altitude.CONCLUSION Over the past 32 years,research on the cardiovascular system in high-altitude regions has been steadily increasing.Future research in this field may focus on areas such as hypoxia adaptation,metabolism,and cardiopulmonary exercise.Strengthening interdisciplinary and multi-team collaborations will facilitate further exploration of the pathophysiological mechanisms underlying cardiovascular changes in high-altitude environments and provide a theoretical basis for standardized disease diagnosis and treatment.

Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains

BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023.By delving into these publications,the aim is to deter-mine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry.METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023,forming the dataset for the analysis.Employing advanced visualization techniques,this study mapped the knowledge domains represented in these papers.RESULTS The findings revealed a prevalent focus on key topics such as depression,mental health,anxiety,schizophrenia,and the impact of coronavirus disease 2019.Additionally,through keyword clustering,it became evident that these papers were predominantly focused on exploring mental health disorders,depression,anxiety,schizophrenia,and related factors.Noteworthy contributions hailed authors in regions such as China,the United Kingdom,United States,and Turkey.Particularly,the paper garnered the highest number of citations,while the American Psychiatric Association was the most cited reference.CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry.Additionally,there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.

Visualization Analysis of Literature on Building Photovoltaic Technology Based on CiteSpace

With the proposal of the double carbon target,the task of energy saving and emission reduction of buildings has become more arduous.The application of building photovoltaic technology is identified as a significant breakthrough to address this challenge.In this paper,the visual analysis and interpretation of literature on building photovoltaic(PV)technology were conducted by using the Cite Space analysis tool based on a review of Chinese and international literature databases.Meanwhile,global research on BIPV technology was summarized and compared.This paper provides ideas for the future application of building photovoltaic technology by constructing a knowledge map for the application of building photovoltaic technology to help the construction of a low-carbon society.

Visual Analysis of the Application and Development Trend of Cinnamon in the Field of Traditional Chinese Medicine Based on CiteSpace

[Objectives]This study was conducted to explore the application and development trend of Chinese medicinal material cinnamon in the field of traditional Chinese medicine.[Methods]Articles published from 2008 to 2023 were exported using"cinnamon"as the subject word in the Chinese database of CNKI.Knowledge graphs were drawn by CiteSpace software on the number of articles published,the institutions publishing articles,and keyword clustering,and the data were sorted by Excel.Combined with the extracted information,the application of cinnamon in traditional Chinese medicine and integrated traditional Chinese and Western medicine was analyzed,and its development trend was discussed and prospected,providing further reference for researchers.[Results]The number of articles published showed an overall upward trend and maintained a high number of articles.In the analysis of the journals publishing articles,the journal with the largest number of articles was West China Journal of Pharmaceutical Sciences,which had certain representativeness.In the analysis of institutions publishing articles,the institution with the largest number of articles was Beijing University of Chinese Medicine,and most institutions had little cooperation.Four categories was obtained in keyword clustering,respectively,general research,component identification,production process and product development of cinnamon."Glycyrrhizin"was the keyword with the earliest burst time,and the hot words that have received much attention in recent years are"medication law"and"data mining".[Conclusions]The application of cinnamon in the field of traditional Chinese medicine is mainly to treat diseases and as raw materials for traditional Chinese medicine products.The development trend is"quality control"and"product research and development".Further research and development of cinnamon in traditional Chinese medicine need to promote the participation of more institutions to participate,and cooperation and communication between institutions should be strengthened to promote the deep integration of production,research and academia.

Visual Analysis of Application and Development Trend of Agarwood Based on CiteSpace

The Chinese traditional medicine,agarwood,is a commonly used medicine for regulating qi,which has many clinical applications because of its unique curative effect.In this study,CiteSpace software was used to visually analyze the application and development trend of agarwood in the literature from CNKI website in the period of 2007-2022.The analysis results showed that the research on agarwood has basically formed core groups of authors,and universities and their affiliated hospitals are main publishing institutions.Moreover,in this study,several research directions of agarwood were also summarized,including clinical research,chemical composition,structural identification and quality standards,showing that agarwood has rich and flexible application prospects in many aspects.On this basis,several suggestions were put forward:strengthening the cooperation between universities and research institutes and building a scientific research cooperation community,and promoting the combination of clinical research and laboratory research.

Bibliometric and visual analysis of post-stroke hemiplegia in the past 20 years

Objective:The purpose of this study is to use CiteSpace software to examine the development patterns and hotspots in post-stroke hemiplegia research during the last 20 years.Methods:Articles about post-stroke hemiplegia from January 2005 to December 2024 were analyzed in the Web of Science database.CiteSpace software was used to conduct co-citation analysis of the articles about post-stroke hemiplegia as well as cooperative network analyses of authors,countries,and institutions,as well as keyword co-occurrence,clustering,emergence,and time line analyses.The results were displayed in a visual atlas.Result:A total of 2625 papers were obtained,and there were more publications overall.China and the United States had the most number of articles,while Chae,John was the author with the most number of articles.Capital Med University and Ohio State University had the most articles among their agencies.The keywords"stroke"and"rehabilitation"were used the most.The key word analysis indicates that the present study focus is on exercise rehabilitation measures and post-stroke treatment.Conclusion:Using CiteSpace software to analyze the literature on post-stroke hemiplegia,it is discovered that the use of new technologies in the field of mechanical thrombectomy and the effectiveness of rehabilitation techniques like mirror treatment are the key areas of study attention.This study has positive significance for finding safer and more effective treatments for post-stroke hemiplegia.

Measurement Analysis and Evaluation of Twenty-Five Years of Mathematics Education Research in China:Visual Econometric Analysis Based on CiteSpace Knowledge Mapping(1999-2024)

Since the new century,China’s mathematics curriculum reform in basic education has continued to move forward in attempts and explorations,presenting many new changes,trends,movements,and developments.Sorting out,analyzing,and summarizing the achievements,experiences,problems,and challenges in this journey are conducive to providing insights for the reform and development of the Chinese basic education mathematics curriculum in the new era.This paper analyses the research on mathematics education in China(1999-2024)using the visual measurement of CiteSpace knowledge mapping,hoping to provide directions for the future of mathematics education in China.

Traditional Chinese Medicine in colorectal cancer treatment:a bibliometrics study and visualization analysis via CiteSpace

Objective:To evaluate the current state of research and areas of interest for traditional Chinese medicine(TCM)in the field of colorectal cancer treatment.Methods:Related papers published between January 1,2012,and November 27,2021,were found using the Web of Science Core Collection Science Citation Index Expanded.Using CiteSpace's network map generation capability,we then determined the top writers,organizations,countries,keywords,co-cited writers,journals,references,and research trends.Results:This investigation yielded a total of 336 relevant papers.China is the most productive country.Shanghai University of Traditional Chinese Medicine is the leading institution.The journal with the most popularity and publishing volume is Evidence-based Complementary and Alternative Medicine.The author with the most citations and centrality is Lin JM.The terms"epithelial-mesenchymal transition,""cell cycle arrest,""apoptosis,"and"autophagy"are highly frequent and have a high betweenness centrality.Conclusion:According to the results,research on natural products,traditional Chinese medicine(TCM)extracts,and the molecular mechanisms of TCM chemical constituents constitutes the primary focus within TCM cancer treatment investigations.In recent years,there has been a surge of interest in exploring the role of gut microbiota in TCM chemical constituents research,particularly in its ability to induce apoptosis and autophagy in tumor cells,thereby suppressing tumor cell proliferation,metastasis,and invasion.However,due to the intricate composition of TCM and existing technical limitations,the underlying principles guiding TCM's efficacy in treating colorectal cancer remain unclear and warrant further investigation.

Application of Bayesian Analysis Based on Neural Network and Deep Learning in Data Visualization

This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data visualization.The research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the needs.Research methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data sets.The results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data interpretation.The significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus(T2DM)is a multifaceted and advancing state that requires further exploration to fully comprehend.Neu-roinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease.AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment.METHODS To identify differentially expressed genes(DEGs)between T2DM and controls,we used data from the Gene Expression Omnibus database GSE125387.To identify T2DM module genes,we used Weighted Gene Co-Expression Network Analysis.All the genes were subject to Gene Set Enrichment Analysis.Protein-protein interaction network construction and machine learning were utilized to identify three hub genes.Immune cell infiltration analysis was performed.The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis.Validation experiments including reverse transcription quantitative real-time PCR,Western blotting and immunohistochemistry were conducted both in vivo and in vitro.To identify potential drugs associated with hub genes,we used the Comparative Toxicogenomics Database(CTD).RESULTS A total of 576 DEGs were identified using GSE125387.By taking the intersection of DEGs,T2DM module genes,and immune-related genes,a total of 59 genes associated with the immune system were identified.Afterward,machine learning was utilized to identify three hub genes(H2-T24,Rac3,and Tfrc).The hub genes were associated with a variety of immune cells.The three hub genes were validated in GSE152539.Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro,consistent with the bioinformatics analysis.Additionally,11 potential drugs associated with RAC3 and TFRC were identified based on the CTD.CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia.We validated the expression of three hub genes both in vivo and in vitro,consistent with our bioinformatics results.We discovered 11 compounds associated with RAC3 and TFRC.These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Brain Functional Network Changes in Patients with Poststroke Cognitive Impairment Following Acupuncture Therapy

Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.

Bibliometric analysis of research in epilepsy and comorbid depression from 2014 to 2023

BACKGROUND Epilepsy and depression have complicated bidirectional relationships.Our study aimed to explore the field of epilepsy comorbid with depression in a bibliometric perspective from 2014-2023.AIM To improve our understanding of epilepsy and depression by evaluating the relationship between epilepsy and depression,bibliometric analyses were performed.METHODS Epilepsy and depression-related publications from the last decade were retrieved from the Web of Science Core Collection.We conducted bibliometric and visual analysis using VOSviewer and CiteSpace,examining authorships,countries,institutions,journals of publication,co-citations of references,connections between keywords,clusters of keywords,and keywords with citation bursts.RESULTS Over the past ten years,we collected 1045 research papers focusing on the field of epilepsy and comorbid depression.Publications on epilepsy and depression have shown a general upward trend over time,though with some fluctuations.The United States,with 287 articles,and the University of Melbourne,contributing 34 articles,were the top countries and institutions,respectively.In addition,in the field of epilepsy and depression,Professor Lee,who has published 30 articles,was the most contributing author.The hot topics pay attention to the quality of life in patients with epilepsy and depression.CONCLUSION We reported that quality of life and stigma in patients with epilepsy comorbid with depression are possible future hot topics and directions in the field of epilepsy and depression research.

Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.

Uncorrected refractive errors are important causes of avoidable visual impairment in Hungary: re-evaluation of two existing national data sets

·AIM: To review existing data for the prevalence of corrected, uncorrected, and inadequately corrected refractive errors and spectacle wear in Hungary.·METHODS: Data from two nationwide cross-sectional studies were analysed. The Rapid Assessment of Avoidable Blindness study collected population-based representative national data on the prevalence of visual impairment due to uncorrected refractive errors and spectacle coverage in 3523 people aged ≥50y(Group I). The Comprehensive Health Test Program of Hungary provided data on the use of spectacles in 80 290 people aged ≥18y(Group Ⅱ).·RESULTS: In Group I, almost half of the survey population showed refractive errors for distant vision, about 10% of which were uncorrected(3.2% of all male participants and 5.0% of females). The distance spectacle coverage was 90.7%(91.9% in males;90.2% in females). The proportion of inadequate distance spectacles was found to be 33.1%. Uncorrected presbyopia was found in 15.7% of participants. In all age groups(Group Ⅱ), 65.4% of females and 56.0% of males used distance spectacles,and approximately 28.9% of these spectacles were found to be inappropriate for dioptric power(with 0.5 dioptres or more). The prevalence of inaccurate distance spectacles was significantly higher in older age groups(71y and above) in both sexes.·CONCLUSION: According to this population-based data, uncorrected refractive errors are not rare in Hungary. Despite recent national initiatives, fur ther steps are required to reduce uncorrected refractive errors and associated negative effects on vision, such as avoidable visual impairment.

Research Characteristics of Thermal Comfort of Urban Block Landscape Based on Knowledge Graph Analysis

The landscape environment of urban blocks plays a significant role in improving the comfort of urban thermal environment and promoting green and high-quality development.The 342 papers related to the research on the impact of urban block landscape environment on thermal comfort in China,collected by CNKI(China National Knowledge Infrastructure)from 2002 to 2022,are used as the research object.Through bibliometric statistical analysis,LLR algorithm,and cluster analysis,the current research status of the impact of urban block landscape environment on thermal comfort in China is analyzed and processed,and its external characteristics are identified.Using the information visualization software CiteSpace,the research topics in the field of the impact of urban block landscape environment on thermal comfort are presented in the form of knowledge graphs.Through co-occurrence analysis of keywords and trend of word frequency changes,the development trends of research hotspots and cutting-edge fields of the impact of urban block landscape environment on thermal comfort are determined,hoping to provide reference for future research in this field.

Visual Analysis of Digital Healthcare Based on CiteSpace

Objective: Digital healthcare is rapidly becoming a new model for medical development in the information society with its convenience, and personalization, and a research boom in digital healthcare has formed at home and abroad in recent years. The purpose of this study is to conduct a bibliometric analysis of the field of digital healthcare and to understand the research background and development trend in this field. Methods: A visual analysis of authors, institutions, journals and keywords was conducted using CiteSpace 5.8R3 software. Results: A total of 1646 digital healthcare-related retrieved from WoS and PubMed studies. There was an overall upward trend in the number of digital healthcare publications, with the highest number of publications in 2021 (290). The author AZIZ SHEIKH is ranked first in the number of published articles (13), while King Saud University (23) is the research institution with the most articles. Keyword clustering showed that the first cluster was data security;the common high frequency keywords that appeared were systems (85), artificial intelligence (82), mobile health (70), internet (61), and technology (57). Digital healthcare, artificial intelligence, healthcare services, machine learning and deep learning are the hotspot of current research. Conclusion: This paper summarises the state of the art in digital healthcare research. Using statistical analysis and network visualisation, it highlights the background, trends and hot topics in digital healthcare research. The paper finds that there is significant potential for artificial intelligence to help bridge the digital divide and reduce health inequalities. To understand the current state, hot trends and future directions of digital healthcare research, this paper can serve as a reference. .

Bibliometric and Visualized Analysis of Cardiac Rehabilitation Research for Coronary Heart Disease Patients

Objective: This study visualizes and analyzes the current status, hotspots, and frontiers of cardiac rehabilitation for patients with Coronary Heart Disease (CHD) over the past decade (2012.01-2022.12), and explores the future development trend and research direction of the CR for CHD patients in China. Methods: Relevant literature was searched, screened and downloaded from the Web of Science (WOS) database, and bibliometric and visualization analyses were performed using CiteSpace VI software. Results: Through the search and screening of related literature, 2443 English articles were finally included. Among them, most of the Chinese publishers were mainly universities and had less connection with each other, while the foreign publishers were mainly universities and medical institutions in the United States and Europe, and had close connection with each other. The research content of Chinese scholars mainly focuses on the assessment of patients’ life and psychological status, as well as the assessment of cardiac function. Foreign research focuses on physical training assessment, disease perception, etc. Conclusion: Through visualizing relevant research with CiteSpace VI software in the form of a knowledge map, the research frontiers and trends in the field of cardiac rehabilitation for coronary heart disease patients in China and abroad can be discovered more intuitively. Compared with foreign research, the development of cardiac rehabilitation for coronary heart disease patients in China is relatively slow and insufficient, and institutions lack cooperation. In the future, China should accelerate the connection between regions in the field of cardiac rehabilitation and develop a cardiac rehabilitation model suitable for coronary heart disease patients in China with its own characteristics.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.