Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022

Introduction

Myocardial infarction (MI), which is colloquially known as a heart attack, is a leading cause of death and disability worldwide (1). Despite significant advances in reperfusion therapies (2), a substantial number of patients still experience recurrent cardiovascular events. Thus, the accurate, early stage evaluation of infarcted myocardium is crucial to enable timely intervention and risk stratification. As medical imaging techniques evolve, non-invasive cardiac imaging, including computed tomography (CT), single-photon emission computed tomography (SPECT), and cardiac magnetic resonance (CMR), has been widely used to diagnose, guide treatment, and assess the prognosis of MI. These techniques are increasingly acknowledged in the medical community (3-5). There has been a significant increase in the publications related to MI imaging across diverse academic fields in recent decades (6,7). However, in the vast literature database, it is often challenging for researchers to gain a comprehensive and integrated understanding of pivotal developments and popular issues in the field.

Bibliometric analysis is an informatics method that employs quantitative techniques to analyze published research in a specific subject area and identify emerging research trends (8). Unlike traditional literature reviews, which are often narrative and subjective, bibliometric analysis provides an objective, data-driven approach for mapping the temporal evolution of research, uncovering patterns in publication outputs, and highlighting key research hotspots (9). Previous studies have employed bibliometric analysis to explore prominent research topics related to MI (10-12). For instance, Edlinger et al. (10) investigated stem-cell therapy for MI from 2001 to 2013 using scientometric methods, and showed the promise of protective paracrine mediators released from transplanted stem cells. Additionally, Xu et al.’s research suggested that criteria and algorithms related to acute myocardial infarction (AMI) in women could be a focal point for future studies (11). By providing a comprehensive view of the research landscape, bibliometric analysis can identify trends that may not be apparent in traditional reviews.

In our study, we aimed to address the current knowledge gaps in MI non-invasive imaging by conducting a systematic bibliometric analysis of 33,480 publications. Using visualization tools such as VOSviewer and CiteSpace, we analyzed publication trends, influential contributors, and key research areas. Our objective was to evaluate the present state of MI non-invasive imaging, highlight emerging trends, and synthesize future research directions to guide clinical practice and promote the advancement of non-invasive imaging technologies for MI.

Methods

Data source and search strategy

We conducted a comprehensive literature search using the Science Citation Index-Expanded tool in the Web of Science Core Collection (WoSCC) database. The WoSCC is an extensive database that includes more than 21,000 peer-reviewed academic journals from over 250 disciplines, including science, social sciences, and humanities. It is considered one of the most comprehensive database platforms for citation analysis and is extensively used in bibliometric research (13). The search period spanned from January 2003 to December 2022. To minimize any potential bias due to database updates, we extracted and downloaded the data in 1 day on August 21, 2023. The following search terms were employed: TS = ((myocardial infarction OR acute myocardial infarction OR myocardial infraction OR myocardium infarction OR myocardial infarct OR myocardial infarcted OR infarction, myocardial OR myocardiac infarct OR cardiovascular stroke OR non-ST elevation myocardial infarction OR ST elevation myocardial infarction OR NSTEMI OR STEMI OR MI OR AMI) AND (cardiac magnetic resonance OR cardiac magnetic resonance imaging OR cardiac MRI OR cardiac MR OR CMRI OR CMR OR magnetic resonance imaging OR MRI OR myocardial perfusion OR LGE OR late gadolinium enhancement OR T1 mapping OR tissue tracking OR echocardiogram OR strain OR ultrasonic OR ultrasound OR ultrasonography OR Doppler OR speckle tracking OR PET OR positron emission tomography OR SPECT OR single-photon emission computed tomography OR single-photon emission OR CT OR computed tomography OR computed tomography OR cardiac nuclide imaging OR myocardial perfusion imaging OR MPI)) AND DOP = (2003-01-01/2022-12-31). Only “articles” and “reviews” written in English were retrieved. Two researchers independently screened the publications. Any disagreements were resolved through collaborative discussions with a senior radiology professor. The search results were exported as a plain text file in “Full Record and Cited References” format, and stored as “download_*.txt”. Ultimately, a total of 33,480 publications were identified for the final analysis. The study flow chart is presented in Figure 1.

Figure 1 Study flow chart. MI, myocardial infarction; WoSCC, Web of Science Core Collection.

Data analysis and visualization

We primarily used CiteSpace (version 6.1.R6) and VOSviewer (version 1.6.18) to conduct the bibliometric and visual analyses of publications in the field of MI imaging research. Additionally, Microsoft Office Excel 2021 (Microsoft, Redmond, WA, USA) was used for the data management and trend analysis of publications. In the Excel file, the time parameters covered the period from 2003 to 2022 with a time interval of one year. Excel 2021 was used to visualize the annual publication trend in terms of the number of articles. CiteSpace is a powerful tool for bibliometric analysis and visualization. It was used to conduct the co-occurrence analysis and visualize the collaborative network of authors, institutions, countries, references, and keywords. It was also used to map dual-map overlays for journals, as well as the strongest citation bursts of references and keywords. In the visual mapping, larger nodes correspond to higher occurrence frequencies. VOSviewer (version 1.6.18) was employed to construct and visualize co-occurrence networks of keywords, and analyze co-authorship networks across countries, authors, and institutions. The bibliometric indicators are detailed in Table S1. The “bibliometrix” package in R software (version 4.0.0) was used for the visual analysis of publications across different global regions.

Results

Annual publication trends

The annual volume of publications is a straightforward but insightful metric for gauging global activity and scientific interest in MI imaging. In our study, we retrieved 33,480 articles from the WoSCC, of which 30,290 were original research articles and 3,190 were review articles. Figure 2 shows the annual publication count and cumulative article count in the field of MI imaging. Notably, with the exception of 2003, the annual publication count consistently exceeded 1,000. Analyzing the data from 2003 to 2013, a stable upward trend in annual publications was observed. A slight decline occurred between 2014 and 2019; however, the overall publication rate remained high from 2020 to 2022, reaching its peak in 2021 with 2,072 articles. To further explore this trend, we fitted the annual cumulative publication count with quadratic function curves using Excel 2021 software. The resulting goodness-of-fit coefficient (R²=0.9965) indicated that MI imaging has garnered substantial scholarly attention over the past two decades. Moreover, it remains a promising research direction in the medical field.

Figure 2 The number of annual publications and cumulative publications on myocardial infarction imaging from 2003 to 2022.

Analysis of countries and institutions

The analysis of publications in the field of MI imaging reveals a global landscape involving 138 countries and 19,554 institutions. In this context, we present the top 10 countries and institutions, as summarized in Table 1. The United States of America (USA) had the highest number of publications (n=11,431) in this area, followed by China (n=4,126), Germany (n=3,362), the United Kingdom (n=2,814), Japan (n=2,771), Italy (n=2,645), The Netherlands (n=2,210), Canada (n=1,719), France (n=1,429), and South Korea (n=1,293). Notably, the combined output from the USA and China accounted for nearly half (46.46%) of the total publications. Figure 3 visually depicts the global geographic distribution of publications, where darker regions indicate higher publication activity. The USA and Europe emerged as the primary hubs of MI imaging research, followed closely by China.

Figure 3 Country scientific production map. The darker the color, the greater the number of documents (frequency) published by a country or region.

Additionally, we employed VOSviewer to filter and visualize countries with five or more publications. The collaborative network in each country is depicted in Figure 4A based on the number and relationships of publications. Notably, the USA engages in active collaborations with several countries, including China, Japan, England, Italy, Germany, and France. Centrality, a critical parameter in the CiteSpace analysis, determines the importance of each node in the collaboration network. Interestingly, among the top 10 countries, none surpassed a centrality value of 0.1 (Table 1 and Figure 4B). This finding suggests that most collaborations were limited to internal connections, and multinational collaborations were relatively scarce.

Figure 4 A visualization map of the countries and institutions. (A) A network map of the countries/regions. The nodes represent countries/regions. The size of the nodes indicates the number of published documents; the more publications, the bigger the nodes. A blue annulus around a node represents high betweenness centrality, illustrating that the node plays an important role in certain fields. (B) A collaborative network of the countries/regions related to MI imaging. The connection between the nodes represents the cooperation between countries/regions; the thickness of the lines shows the level of the combinations. (C) A network map of the institutions. (D) Cooperative relationships among institutions in relation to MI imaging. MI, myocardial infarction.

The investigation into MI imaging encompassed a vast network of 19,554 institutions. Among the top 10 institutions (Table 1), six hailed from the USA. The top 5 institutions were Harvard University (n=1,677; 5.01%), the University of California System (n=1,297; 3.87%), Harvard Medical School (n=1,063; 3.18%), Johns Hopkins University (n=852; 2.54%), and Université Digitale et Innovante pour les Collèges Européens (UDICE)-French Research Universities (n=815; 2.43%). Harvard University’s prolific output underscores its prominence in MI imaging research. The University of California System contributed significantly to the field. Notably, with a centrality value of 0.11, Johns Hopkins University holds a critical bridging role in MI imaging research. Figure 4C,4D show the network map of institutions and the cooperative relationships among institutions in the realm of MI imaging.

Analysis of authors and co-cited authors

In the domain of MI imaging, a comprehensive analysis of author cooperation and author co-citation patterns provided valuable insights into core scholars and research teams, collaborative networks, and other pertinent information. Our study comprised 146,043 authors who have contributed significantly to this field. The top 10 authors and co-cited authors ranked by publication count are listed in Table 2. Budoff stands out as the most prolific contributor (n=209, 0.62%), followed by Stone (n=193, 0.58%) and Mintz (n=179, 0.53%). However, Table 2 reveals an intriguing trend; only five of the top 10 authors had centrality values exceeding 0.1. This observation suggests that collaboration among research teams in this field remains limited. The cooperation network among authors is visually depicted in Figure 5A.

Figure 5 Visualization map of authors (A) and co-cited authors (B) conducting research on MI imaging. MI, myocardial infarction.

Additionally, we examined the co-citation relationships. A co-cited relationship refers to the concurrent citation of two or more authors on one or more articles. Such authors are collectively referred to as co-cited authors. This analysis provided valuable insights into collaborative networks and influential connections in a specific field. Among the 269,597 co-cited authors, seven emerged as prominent figures, each cited no fewer than 1,500 times (Table 2). The top co-cited author was Kim (n=2,794), followed by Stone (n=2,483), and Cerqueira (n=1,697). Figure 5B presents a co-citation network graph, highlighting influential connections among these authors.

Analysis of journals and co-cited journals

A total of 3,058 journals have contributed articles or reviews in the field of MI imaging. Among the leading 10 journals (Table 3), the American Journal of Cardiology published the highest number of articles (n=763), followed by the Journal of the American College of Cardiology (n=722), and the International Journal of Cardiology (n=650). Notably, the journal Circulation boasts the highest impact factor (IF =37.8, Q1). Additionally, the majority of these top 10 journals fall into the Journal Citation Reports (JCR) Q1 and Q2 categories, highlighting their significant academic influence.

In terms of the co-citation analysis, the top 10 co-cited journals are also presented in Table 3. Seven journals had been cited more than 20,000 times. Circulation led the way with a remarkable citation frequency of 133,554, followed by the Journal of the American College of Cardiology (n=96,840), the New England Journal of Medicine (co-citation =39,549), and the American Journal of Cardiology (co-citation =37,874). Further, the journal Lancet boasted the highest IF (IF =168.90), closely followed by the New England Journal of Medicine (IF =158.50).

To visualize the relationships between citing and the cited journals, we employed a dual-map overlay in CiteSpace. In Figure 6, the left side represents the citing journals, while the right side represents the cited journals. The hues of the connecting lines indicate the disciplines covered by these journals. Notably, four main citation paths stand out, highlighted in green and yellow. The green paths indicate that research published in health/nursing/medicine and molecular/biology/genetics journals is frequently cited by publications in medical/clinical journals. The yellow paths reveal that documents from molecular/biology/genetics and health/nursing/medicine journals are predominantly cited by publications in molecular/biology/immunology journals.

Figure 6 The dual-map overlay of journals in the field of MI imaging. MI, myocardial infarction.

Analysis of keywords

Keyword analysis plays a pivotal role in comprehending the fundamental content of research and uncovering the prevailing themes and emerging trends in a field. We present the top 20 high-frequency keywords (Table 4) in the context of MI imaging. The popular topics were MI, risk factors, AMI, magnetic resonance imaging (MRI), and atherosclerosis. These high-frequency keywords collectively represent the primary research directions and areas of focus in the field of MI imaging.

To enhance the clarity of our keyword map, we employed VOSviewer for the cluster analysis. After eliminating duplicates, our analysis comprised 58,770 keywords extracted from 33,480 articles. Among these, 428 keywords appeared more than 110 times and were used to construct a visualization map. The results, as depicted in Figure 7A, revealed five distinct clusters. Each cluster represents a specific research direction, and the node size reflects the frequency of keyword occurrence. Cluster 1 (red) comprised 118 items related to qualitative and quantitative evaluation using MRI and echocardiography, as well as topics related to heart failure and dysfunction. Cluster 2 (green) comprised 104 items related to risk factors, atherosclerosis, and clinical features. Cluster 3 (blue) comprised 83 items related to AMI and reperfusion treatments (including thrombolysis and percutaneous coronary intervention), as well as their outcomes. Cluster 4 (yellow) comprised 65 items mainly related to inflammation, heart function, expression, and management. Cluster 5 (purple) comprised 58 items related to coronary artery disease, CT, and prognostic value.

Figure 7 Visualization of keywords. (A) Clustering map of keywords. The keywords were divided into five clusters with different colors, with the node size indicating the frequency of occurrence. (B) Timeline view of the keyword cluster analysis related to MI medical imaging. (C) The top 25 most cited keywords. The blue section represents the time period in which the citation was published, and the red section represents the time period in which the reference burst occurred. MI, myocardial infarction.

The keyword timeline view allowed us to visualize the period and time trends for different research keywords. Figure 7B displays the clusters of keywords and identification of research hotspots. The following 18 distinct clusters were established (Table 5): “#0: echocardiography”, “#1: coronary artery disease”, “#2: stem cells”, “#3: percutaneous coronary intervention”, “#4: acute coronary syndrome”, “#5: cardiac magnetic resonance”, “#6: infarction”, “#7: positron emission tomography”, “#8: cardiovascular disease”, “#9: risk factors”, “#10: carotid artery”, “#11: primary angioplasty”, “#12: risk stratification”, “#13: task force”, “#14: blood pressure”, “#15: magnetic resonance imaging”, “#16: heart failure”, and “#17: myocardial ischemia”. The modularity Q value of 0.8884 (which was greater than 0.3) indicated that the community structure of clustering was statistically significant. Additionally, the silhouette score value of 0.9833 (which was greater than 0.7) provided further evidence that the clustering results were reliable.

Burst keywords are those that exhibit frequent occurrence in a short period. These keywords serve as indicators of research hotspots and changing trends in a field during different periods, and can foreshadow future research directions. Figure 7C shows the top 25 keywords with the most burst citations. “European association” had the highest burst strength (92.41), while “congestive heart failure” had the longest duration (from 2003 to 2010). Keywords such as “outcome”, “STEMI”, “computed tomography angiography (CTA)”, “guidelines”, “recommendations”, and “society” have garnered significant attention in recent years, reflecting emerging research trends in MI imaging, and may evolve into prominent research hotspots in the future.

Analysis of cited references

A comprehensive collection of 569,880 references was obtained, and the top 10 co-cited references along with their respective co-citation counts are presented in Table 6. Notably, four influential manuscripts originated from the European Heart Journal. Among these, the most frequently cited article, which was titled “2017 ESC Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting with ST-Segment Elevation”, authored by Ibanez et al. (14), and published in the journal KARDIOL POL in 2018, had 310 citations. The timeline view of the co-cited references revealed 15 distinct clusters (Figure 8A). The largest cluster was “#0 stem cell”, followed by “#1 myocardial infarction”, and “#2 subclinical atherosclerosis”. “#1 myocardial infarction” and “#9 SCMR guideline” emerged as prominent research hotspots in recent years. Further, Figure 8B shows the top 25 references with the strongest citation bursts. Early references with citation bursts can be traced back to a publication in Radiology in 2003.

Figure 8 Visualization of the co-cited reference analysis. (A) The timeline view of publications related to MI imaging with relevant clusters. (B) Top 25 references with the strongest citation bursts in the field of MI imaging. The blue section represents the period when the citation was published, while the red section indicates the timeframe during which the reference burst occurred. MI, myocardial infarction.

Discussion

To our knowledge, this study represents the first comprehensive bibliometric analysis of MI imaging. We retrieved 33,480 articles published between 2003 and 2022 from the WoSCC database. Using CiteSpace and VOSviewer, we analyzed publication trends, collaborative networks, thematic hotspots, and primary research frontiers. Our aim was to deepen our understanding of non-invasive imaging applications in MI and provide guidance for future research.

General information

The publication output concerning MI imaging studies displayed a consistent upward trajectory from 2003 to 2022. The number of articles has surged to over 1,000 since 2004, with a remarkable spike of 2,072 in 2021. This surge indicates that MI imaging has become a hot research topic in the cardiovascular field. Several factors contributed to this growth. First, MI, as the leading cause of morbidity and mortality among all cardiovascular diseases, has consistently remained a central point of investigation (1). Second, the non-invasive nature of medical imaging has made it a crucial tool for comprehensive diagnosis, prognosis, and treatment evaluation in MI, significantly advancing research in this domain (15). Finally, the urgent need to explore MI causes and treatments has led to a substantial expansion in the existing literature (16,17). Notably, there was an increase in publications in 2020, which was likely influenced by the coronavirus disease 2019 pandemic, which provided novel mechanistic insights and continued to drive academic progress in this field (18).

Research on MI imaging is being conducted across various countries worldwide. In the analysis of countries/regions and institutions, the USA had the highest number of published articles (n=11,431, 34.14%). Notably, with the exception of China, all of the top 10 countries in terms of publication volume were developed nations. This suggests that China should focus on strengthening its international cooperation and improving research output in this field. Further, 6 of the 10 most productive institutions were located in the USA. Harvard University, which had 1,677 publications (5.01%), led the pack. This dominance explains why the USA wields significantly greater influence than any other country. However, when examining the centrality of collaborations, only Johns Hopkins University (0.11) surpassed the threshold of 0.1. This indicates that most collaborations are internal, and engagement with global partners remains relatively limited. This highlights an important area for improvement. For example, due to differences in coronary calcium distribution, the performance of artificial intelligence (AI) models trained on Western populations is unknown in Asian cohorts (19).

Fostering international collaboration and communication among institutions is essential for advancing the non-invasive imaging in MI for several key reasons. First, international collaboration facilitates the exchange of diverse perspectives, expertise, and resources, which accelerates innovation. For example, an international multicenter, multi-vendor study showed that contrast-enhanced steady-state free precession magnetic resonance imaging (CE-SSFP) provided higher diagnostic quality images in patients with ST-elevation myocardial infarction (STEMI) than T2 imaging. This finding suggests that CE-SSFP is better suited for implementation in multicenter, multi-vendor clinical trials (20). Second, in fields such as MI non-invasive imaging, advancements in technology, particularly AI, require interdisciplinary and cross-border collaboration to fully harness their potential. In an international, multicenter study, a fully automated machine learning–based late gadolinium enhancement analysis reliably quantified myocardial scar mass and improved the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation (21). Further, engaging with global partners enables researchers to address MI non-invasive imaging as a global health issue, enhancing the generalizability of findings to diverse populations and healthcare systems. For instance, Tzimas et al. employed an AI-enabled quantitative coronary plaque analysis tool to analyze plaque values from a large international multicenter cohort. This study developed age- and sex-stratified percentile nomograms for atherosclerotic plaque measures, which are expected to be integrated into clinical decision-making processes (22).

Based on the analysis of authors and co-cited authors in related fields, Budoff, from the University of California, Los Angeles, who had 209 publications (0.62%), emerged as the most prolific author. Budoff’s research is dedicated to advancing procedures that aid in the early identification of patients at high risk of cardiac events and atherosclerosis progression (23,24). Early detection enables timely intervention, which could in turn prevent heart attacks. Additionally, Budoff’s work centers on cholesterol management and cardiac CT scanning for preventive cardiology, including risk factor identification and modification (25). Notably, Kim, from Duke University Medical Center, who had 2,794 citations, was the most frequently co-cited author. His pivotal contributions focus on the application of CMR in patients with MI. Recently, Kim reported that unlike conventional delayed enhancement CMR, flow-independent dark-blood delayed enhancement CMR shows exceptional accuracy (100%) in detecting papillary muscle infarction (26). While leading authors like Budoff and Kim have made seminal contributions independently, synergistic collaborations could address unresolved clinical challenges. For instance, compared to routine clinical care, the integration of Budoff’s CT-based plaque analysis and Kim’s CMR technique could improve diagnostic accuracy and reduce the number of invasive coronary angiography (ICA) procedures, while also enhancing appropriate referrals to ICA without negatively affecting clinical outcomes (27). Recently Pezel et al. applied machine-learning techniques to integrate data from cardiac MRI and coronary CTA to predict major adverse cardiovascular events in patients diagnosed with obstructive CAD. Their findings revealed that the machine learning–based model achieved a higher area under the curve (0.86) for predicting major adverse cardiovascular events than the European Society of Cardiology (ESC) score (0.55), QRESEARCH risk estimator version 3 (QRISK3) score (0.60), Framingham risk score (0.50), CTA alone (0.76), and CMR alone (0.83) (28). Thus, strengthening such collaborative efforts could facilitate the application of scientific advancements in clinical practice, thereby further advancing progress in the field.

In relation to journals, the American Journal of Cardiology stood out with 763 publications, making it the most prolific journal in this field. Impressively, 80% of its publications ranked as JCR Q2 or higher among the top 10 journals. Additionally, the journal Circulation not only boasted the highest number of citations but also maintained a commendable IF. Further, high-impact journals, such as the New England Journal of Medicine (29), Lancet (30), and European Heart Journal (31), have also contributed articles related to MI imaging. These journals play a pivotal role in advancing MI imaging research by disseminating high-quality scientific findings.

In our bibliometric analysis, the top 10 cited manuscripts were predominantly related to the ESC guidelines, cardiac imaging, and coronary atherosclerosis. These influential articles primarily originated from European countries and the USA, emphasizing the substantial contributions of scholars from these regions to MI imaging research. Among these notable works, the most frequently cited article was published in 2018 and was authored by Ibanez et al. (14), affiliated with the Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain. This study provided essential guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Additionally, a pivotal reference with a citation burst dates back to 2001 when Simonetti et al. (32) published their groundbreaking work in radiology. Their article introduced a novel technique for visualizing MI (referred to as the segmented inversion-recovery turbo fast low-angle shot MRI pulse sequence). The application of this technique has significantly enhanced the differentiation between injured and normal myocardial areas in patients with MI.

Research hotspots and frontiers

Keywords in the literature play a crucial role, as they summarize research content and help identify research hotspots. In our study, the keyword analysis revealed that the five most frequently used terms were MI, risk factor, AMI, MRI, and atherosclerosis. The primary etiology of MI is atherosclerotic plaque rupture and subsequent thrombosis. Many risk factors for MI are closely associated with atherosclerosis (33). The diagnosis of MI mainly relies on electrocardiogram and serum marker detection; however, with the advancement of imaging technology, CMR has emerged as a research hotspot in MI imaging research. CMR has excellent soft tissue resolution, is radiation-free, and has multi-parameter, multi-plane, and multi-sequence imaging capabilities. It allows for the detailed assessment of MI morphology, function, myocardial edema, intramyocardial hemorrhage, surviving myocardium, microvascular obstruction, and fibrosis extent (34-36). Overall, CMR provides vital information for clinical diagnosis and therapy, enhancing the safety and accuracy of assessing cardiac injuries.

The results of the keyword cluster analysis shed light on the core issues and developmental trajectories in this field. Early studies primarily focused on “#8 cardiovascular disease”, “#1 coronary artery disease”, “#3 percutaneous coronary intervention”, and “#9 risk factors”. Conversely, the current hot topics include “#0 echocardiography”, “#3 percutaneous coronary intervention”, “#4 acute coronary syndrome”, “#5 cardiac magnetic resonance”, “#9 risk factors”, “#16 heart failure”, and “#17 myocardial ischemia”. The primary cause of MI is vascular occlusion resulting from atherosclerotic plaque rupture. Percutaneous coronary intervention and thrombolytic therapy remain the preferred reperfusion strategies, effectively reducing myocardial ischemia, infarct size, and the risk of complications and heart failure post-MI. Echocardiography, an easily accessible and cost-effective non-invasive imaging tool, enables the extent of ischemic myocardial damage to be assessed (37) and has functional implications. Parameters such as the left ventricular ejection fraction, wall motion score index, E wave to e’ wave (E/e’) ratio, global longitudinal strain, and ventricular-arterial coupling provide valuable information for risk stratification after MI (38-40). In recent years, following the development of automated CMR analysis methods, AI, machine learning, and deep learning in particular have been used to build tools that are efficient, robust, accurate, and accessible to clinicians, which have improved both clinician productivity and the quality of patient care (41). Global collaborative efforts through data sharing will further propel the field’s progress.

Through the analysis of keyword bursts, researchers can uncover and identify research hotspots and emerging trends in a specific field. During the pivotal years of 2003 to 2008, several critical research directions emerged in cardiovascular medicine. These included the investigation of viability, doppler echocardiography, congestive heart failure, and beam CT. Researchers primarily focused on detecting and evaluating myocardial viability following infarction during this period. As research advanced, attention shifted toward post-infarction myocardial repair and the application of delayed enhancement techniques to assess the extent of MI. From 2017 to 2022, the research community exhibited a growing interest in specific keywords such as STEMI, CTA, guidelines, recommendations, and society.

CTA, a convenient and non-invasive imaging method, enables the visualization of plaque and wall features, including lumen stenosis. Notably, novel technologies, including the fat attenuation index for early coronary inflammation detection, have demonstrated incremental value in predicting adverse clinical events beyond traditional risk factors and the CTA index (42). Additionally, CT-based fractional flow reserve-CT has emerged as a non-invasive index of coronary artery flow function (43). One study demonstrated that fractional flow reserve-CT, which had area under the curve values of 0.94 and 0.92 for the identification of ischemia-causing lesions on a per-vessel and per-patient level, respectively, performed significantly better than coronary CTA (AUCs: 0.83 and 0.81) and SPECT (AUCs: 0.70 and 0.75) (44). Looking ahead, the ongoing development of multimodal imaging is likely to represent a transformative trend in this field (45).

In recent years, an expanding body of research has underscored the pivotal role of non-invasive cardiac imaging in the early diagnosis, non-invasive risk stratification, and prognostic assessment of patients with MI. By leveraging advanced imaging techniques, clinicians can define optimal management strategies, thereby improving patient prognosis and tailoring individualized treatment plans (46). Further, the rapid advancements in AI offer exciting possibilities. AI-driven algorithms can identify high-risk patients at an early stage, allowing for efficient resource allocation and targeted interventions (47,48).

Limitations

Our study provided a comprehensive overview of the research field related to MI imaging; however, it had some limitations. First, our dataset was exclusively sourced from the WoSCC database, overlooking other crucial repositories such as PubMed and Google. This selective approach might have resulted in the inadvertent omission of relevant research findings. Second, our use of CiteSpace facilitated a general assessment of the current state of MI imaging, but it did not involve a thorough review of full-text articles. Consequently, certain nuanced details might have been overlooked. Third, the restriction of the retrieved articles to English-language publications introduces the possibility of research bias. Fourth, the publication counts for institutions were based on the affiliations of all co-authors, not just the first author. This methodology might have resulted in overlap and potential double-counting, especially when the authors listed both a medical school and its affiliated hospital as separate affiliations. Such overlap might introduce biases in the estimation of institutional influence.

Conclusions

MI imaging has significant research value and promising application prospects. The increased publication frequency in international core journals underscores the significant impact of MI imaging research. The USA continues to lead in MI imaging research, boasting highly productive institutions and a substantial number of core authors. This leadership is expected to persist in the future. Budoff emerged as the most prolific author, while Kim held the distinction of being the most influential in terms of co-citations. The American Journal of Cardiology was the journal with the highest number of publications in this area. Collaborations and exchanges between top authors and institutions from different countries will help drive progress in this field. Currently, the main areas of focus in MI imaging research include STEMI, CTA, guidelines, recommendations, and outcomes. In terms of future developments, multimodal imaging is poised to emerge as a transformative trend in MI imaging. Integrating diverse imaging modalities should improve diagnostic accuracy and provide a holistic understanding of MI.

Acknowledgments

The authors would like to acknowledge that they used CiteSpace (Version 6.1.R6) and VOSviewer (Version 1.6.18) for the visualization and analysis of the bibliometric data presented in this work.

Footnote

Funding: This study was supported by grants from the National Natural Science Foundation of China (No. 82260342), the China Scholarships Council (No. 202306820088), and the Jiangxi Provincial Key Natural Science Foundation of China (No. 20212ACB206021).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-878/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All data were obtained through literature retrieval based on the Web of Science Core Collection database. No medical institutions or patients were included, and thus ethical approval or informed consent was not required.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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