journal of ophthalmology research reviews and reports

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Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017–2021

1 State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China

2 Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China

3 Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China

Weining Zhu

4 Zhongshan Medical School, Sun Yat-sen University, Guangzhou, China

Yingshi Zou

Bowen zhang, guangming jin, zhenzhen liu, associated data.

Publicly available datasets were analyzed in this study. This data can be found here: https://www.webofscience.com/wos/alldb/basic-search .

The purpose of this study was to investigate the hotspots and research trends of ophthalmology research.

Ophthalmology research literature published between 2017 and 2021 was obtained in the Web of Science Core Collection database. The bibliometric analysis and network visualization were performed with the VOSviewer and CiteSpace. Publication-related information, including publication volume, citation counts, countries, journals, keywords, subject categories, and publication time, was analyzed.

A total of 10,469 included ophthalmology publications had been cited a total of 7,995 times during the past 5 years. The top countries and journals for the number of publications were the United States and the Ophthalmology. The top 25 global high-impact documents had been identified using the citation ranking. Keyword co-occurrence analysis showed that the hotspots in ophthalmology research were epidemiological characteristics and treatment modalities of ocular diseases, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of ocular diseases. Keyword burst analysis revealed that “neural network,” “pharmacokinetics,” “geographic atrophy,” “implementation,” “variability,” “adverse events,” “automated detection,” and “retinal images” were the research trends of research in the field of ophthalmology through 2021. The analysis of the subject categories demonstrated the close cooperation relationships that existed between different subject categories, and collaborations with non-ophthalmology-related subject categories were increasing over time in the field of ophthalmology research.

Conclusions

The hotspots in ophthalmology research were epidemiology, prevention, screening, and treatment of ocular diseases, as well as artificial intelligence and fundus imaging technology and telemedicine. Research trends in ophthalmology research were artificial intelligence, drug development, and fundus diseases. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

Introduction

More than 2.2 billion people worldwide were visually impaired or blind to date, with an annual economic burden of more than $269.4 billion ( 1 ). Development in ophthalmology is essential for the prevention and treatment of eye diseases, and relevant research is growing rapidly in breadth and depth and forming complex knowledge networks. Glaucoma, age-related macular degeneration, and some hereditary eye diseases were previously considered irreversible blindness-causing diseases, and progress had been made to cure or alleviate them by modulating new targets or using new technologies ( 2 – 4 ). Cataracts and posterior capsular opacification were previously thought to be treated only with surgery, but in the recent years, there had been new developments in research into drugs that inhibit cataract formation ( 5 , 6 ). With the advances in the field of ophthalmology, new hope has emerged in areas previously considered untreatable or treatable only through non-pharmaceutical interventions ( 7 – 10 ). However, it is not feasible to analyze the overall overview of the field of ophthalmology and to explore its research hotspots and trends with a traditional systematic review, which is not conducive to the development of the field.

Bibliometric analysis is the quantitative analysis of the universal scientific production data in a specific field ( 11 ). Bibliometric method obtains the history and current status of the research field development by analyzing the scientific research results and can make predictions of the research field ( 12 ). Previous studies have conducted bibliometric analysis on individual country contributions or focused only on randomized controlled studies in ophthalmology and citation patterns in ophthalmology journals ( 13 – 19 ). Unsolved questions still remain as to how to quantitatively evaluate the contribution of different global research forces (countries, journals) in ophthalmology and identify hotspots and future research trends in ophthalmology based on a wide range of research results in different subfields of ophthalmology.

This study was intended to quantitatively analyze and visualize the global ophthalmology publication from 2017 to 2021 using bibliometric methods to explore the global research forces (countries, journals), possible hotspots, and future trends of ophthalmology research and to provide insight for research development and public health policy formulation in the field of ophthalmology.

Data sources

All the data used in this study were obtained from the Web of Science Core Collection (Clarivate Analytics, Philadelphia, PA, USA). The search was conducted by searching the Topic Subject retrieval field using “ophthalmology” as the subject word. Articles published between 2017 and 2021 were included, with no restrictions on the language type or document type of the articles. Data were collected on 28 January 2022.

Data collection and processing

To describe the number of articles published per year, the number of annual citations of the articles, the number of country publications, and the number of journal publications in the field of ophthalmology, relevant data were downloaded in the Web of Science Core Collection. All ophthalmology-related articles with their corresponding references and all publication-related information were exported as plain text for country collaboration analysis, keyword co-occurrence analysis, keyword burst analysis, and subject category co-occurrence analysis. To make the results more informative, keywords that were not relevant or meaningful to the analysis were filtered and removed during the data processing.

Statistical and bibliometric analysis

Statistical descriptions of the number of annual publications, the number of annual citations, the number of country publications, and the number of journal publications were performed using Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA) and GraphPad Prism version 8.4.2 (GraphPad Software, La Jolla, CA, USA).

Bibliometric analysis was carried out using VOSviewer (Leiden University's Centre for Science and Technology Studies, Leiden, the Netherlands) to obtain country collaborations and research hotspots. Several clusters were formed based on the country cooperation analysis, with countries of the same color belonging to the same cluster. Countries within clusters cooperated relatively closely, whereas cooperation among countries between clusters was relatively weak. The research hotspots were obtained from the clusters formed by the co-occurrence analysis of high-frequency keywords. The common characteristics of high-frequency keywords within the same cluster revealed the research hotspots. The frequency of keyword occurrences was used to weight the size of the keywords. The larger the keyword, the higher the frequency of occurrence.

Furthermore, CiteSpace V version 5.8.R3 (Drexel University, Philadelphia, PA, USA) was used for bibliometric analysis to obtain the burst keywords and subject category cooperation. The keyword burst analysis was performed to obtain temporal trends in keywords in the field of ophthalmology. The most recent burst keywords were defined as research frontier topics, indicating the potential for continued research breakthroughs in these topics. The co-occurrence of subject categories was analyzed to obtain the collaboration of subject categories. The number of occurrences of a subject category was used to weight the subject category. The more occurrences a subject category had, the larger it was. Temporal trends in subject category occurrences were represented by temporal rings of subject categories, the thickness of which represented the number of subject category occurrences in the corresponding year. Interdisciplinary cooperation was represented by the connecting line between subject categories. The thicker the connecting line, the closer the collaboration.

Global research output distribution

A total of 139 countries contributed to the publications related to ophthalmology research, with a total of 10,469 articles, which were cited 7,995 times. The number of publications had increased year by year, but there was an inflection point in citation counts. Citation counts increased year by year from 2017, reaching 2,650 citations in 2020, whereas citations in 2021 decreased compared to 2020 ( Figure 1A ). The analysis of countries showed that the United States had the highest number of publications, more than three to four times the number of other countries, followed by the United Kingdom, India, Germany, and China ( Figure 1B ). Country collaboration analysis yielded four clusters, with close cooperation between countries within each cluster ( Figure 1C ). Publications related to ophthalmology research were distributed in 1,876 journals, and the top 10 journals in terms of the number of articles published were the Ophthalmology ( n = 1,263, 12.06%), the Ophthalmology. Retina ( n = 580, 5.54%), the BMJ Case Reports ( n = 270, 2.58%), the Journal of Neuro-Ophthalmology: the official journal of the North American Neuro-Ophthalmology Society ( n = 260, 2.48%), the Investigative Ophthalmology & Visual Science ( n = 214, 2.04%), the Ophthalmology, Glaucoma ( n = 204, 1.95%), the Journal of Current Ophthalmology ( n = 200, 1.91%), the European Journal of Ophthalmology ( n = 191, 1.82%), the Indian Journal of Ophthalmology ( n = 173, 1.65%), and Journal of Cataract and Refractive Surgery ( n = 171, 1.63%) ( Figure 1D ).

Global distribution of research output. (A) Annual publications and citations of ophthalmology research from 2017 to 2021. (B) Top 10 countries in terms of total publications. (C) Country cooperation networks. (D) Top 10 journals by total publication volume of ophthalmology research in a 5-year period.

Global high-impact documents

The top 25 high-impact articles in ophthalmology published between 2017 and 2021, ranked by total citations, are shown in Table 1 . All the articles had been cited more than 150 times, with the highest number of citations being 419. Of these articles, 10 were published in 2017, 12 in 2018, one in 2019, and two in 2020. In total, 12 of these articles were published in the Ophthalmology and three in the Progress in Retinal and Eye Research. According to the type of publication, there were 16 original research articles and 9 review articles. The keywords involved in the articles are listed in Table 1 , including 5 articles each on OCT and deep learning, 4 articles each on diabetes and macular degeneration, and other related research topics such as glaucoma, artificial intelligence, and drugs.

Top 25 most cited documents published between 2017 and 2021.

Research hotspots

Keyword co-occurrence analysis demonstrated that the three most frequent of all keywords were “glaucoma” ( n = 395), “retina” ( n = 321), and “optical coherence tomography” ( n = 230). In the past 5 years, 157 high-frequency keywords in the field of ophthalmology were identified by setting the minimum frequency of keyword occurrence at 20 times. These keywords formed four clusters: the “glaucoma” cluster (red; 86 items), the “retina” cluster (green; 47 items), the “COVID-19” cluster (blue; 13 items), and the “screening” cluster (yellow; 8 items) ( Figure 2 ). After summarizing the keyword clusters, four research hotspots were identified: epidemiological characteristics and treatment modalities of diseases such as glaucoma and diabetic retinopathy, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of eye diseases.

Ophthalmology research hotspots analysis. The keywords formed four clusters, which were differentiated by color in the diagram, with the same color being the same cluster. The keyword size indicated the number of occurrences of the keyword, whereas the thickness and distance of the connecting lines between the keywords indicated the frequency of co-occurrence between the two keywords.

Research trends

Keyword burst analysis showed that “neural network,” “pharmacokinetics,” “geographic atrophy,” “implementation,” “variability,” “adverse events,” “automated detection,” and “retinal images” were the hot topics of research in the field of ophthalmology through 2021 and displayed the potential to become the research frontiers to achieve breakthroughs shortly ( Figure 3A ).

Ophthalmology research trends analysis. (A) Keyword burst analysis. The red line indicates the year in which the burst of the corresponding keyword began and ended. (B) Subject category analysis. The larger subject categories indicate their greater frequency and importance, and the distance between subject categories indicates how closely they collaborate. The lines between subject categories indicate the collaboration between the subject categories at either end, with the color of the different lines representing the collaboration time in the different subject categories and the thickness representing the degree of collaboration closeness. The color of the temporal rings represents the occurrence of that subject category in different years, the thicker the corresponding temporal rings, the more frequently it occurs, with the time scale at the bottom right.

In terms of subject categories, the top three subject categories with the highest volume of ophthalmology-related research publications were medicine general internal ( n = 1,138, 10.87%), clinical neurology ( n = 482, 4.604%), and surgery ( n = 368, 3.515%) ( Table 2 ). The subject categories of ophthalmology research were divided into two types: one was the traditional ophthalmology-related subject categories, such as medicine general internal, clinical neurology, and surgery, and the other one was the non-ophthalmology-related subject categories, such as engineering, computer science, and chemistry. The analysis of subject category collaboration relationships indicated that over time more collaborative relationships had emerged between non-ophthalmology-related subject categories ( Figure 3B ).

Subject categories in ophthalmology from 2017 to 2021.

Research in the field of ophthalmology showed a year-on-year increase in the number of articles published in the last 5 years, with the most published country being the United States and the most prolific journal being the Ophthalmology. The top 25 high-impact articles worldwide were cited more than 150 times per article. A total of four research hotspots were identified: epidemiological characteristics and treatment modalities of diseases such as glaucoma and diabetic retinopathy, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of eye diseases. Cross-talk between different non-ophthalmology subject categories was also an important trend in ophthalmology.

The annual publication volume, country distribution, and journal distribution of the ophthalmology research articles revealed a global overview of research output in the field of ophthalmology. The output of ophthalmology research showed an increasing trend in the last 5 years, suggesting that the socioeconomic input and scientific output of the subject area were also developing ( 20 ). The individual contributions of some countries to ophthalmology research were previously reported, but there were limitations on the overall evaluation of all countries' contributions to ophthalmology research and of country collaboration ( 13 – 17 ). This study showed that the predominant countries in ophthalmology research included the United States, the United Kingdom, and India, and countries such as Germany, China, and Australia also played an important role in the contribution. Several stable collaborative networks have been formed between countries, which can facilitate cross-border research data sharing and the globalization of scientific research. The top five most published journals showed that ophthalmology research was mainly focused on clinical ophthalmology (Ophthalmology, BMJ Case Reports), basic ophthalmology research (Investigative Ophthalmology and Visual Science) and neuro-ophthalmology (Journal of Neuro-Ophthalmology, Ophthalmology Retina).

The high-impact articles in ophthalmology indicated that researchers in the field of ophthalmology were primarily concerned with ophthalmological health or disease states, as well as ophthalmological technologies and applications. In terms of health or disease conditions, age-related macular degeneration ( 21 – 24 ), glaucomatous optic neuropathy ( 25 , 26 ), corneal blindness ( 27 ), and other blinding eye diseases occupied important research positions. Research directions such as screening for diabetic retinopathy ( 28 , 29 ), preventing myopia ( 30 ), optimizing visual outcomes, and controlling complications after IOL implantation following cataract surgery were dedicated to the active identification, management, and control of disease risk factors, making the eye disease controllable and manageable ( 31 , 32 ). In addition, researchers were also concerned with the management of Behcet's syndrome ( 33 ) and COVID-19 infection prevention in ophthalmology ( 34 ). In ophthalmology-related technologies, the frontiers were artificial intelligence algorithms ( 23 , 25 , 26 , 35 – 38 ), new pathways for drug delivery ( 39 , 40 ), and new materials for therapy ( 41 ). In ophthalmology-related applications, the pioneering applications were optical coherence tomography ( 23 , 24 , 35 , 42 – 44 ), stem cell therapy, and tissue repair ( 45 ).

After clustering the high-frequency keywords in the past 5 years, four research hotspots in the field of ophthalmology were obtained. First, the epidemiological characteristics and treatment modalities of diseases such as glaucoma and diabetic retinopathy were the hot topics of ophthalmology research. The emergence of these hot topics was consistent with the increasing prevalence of systemic chronic diseases such as diabetes in the last 5 years, and several studies have revealed associations and common biomarkers of ophthalmology and systemic diseases ( 46 – 49 ). More future work needs to further focus on the diagnosis and optimal treatment strategies for blinding diseases associated with systemic conditions ( 50 ). Moreover, deep learning algorithms that could rapidly and non-invasively identify pathological features of eye diseases joined ophthalmology research ( 23 ). Deep learning algorithms could classify age-related cataract types based on slit-lamp photographs, and fully automated AI-based screening systems had been approved for the use in diabetic retinopathy ( 37 , 51 ). Furthermore, the emergence of the COVID-19 pandemic brought about an increase in the length of patient visits due to disease control and health-related problems associated with COVID-19 infections, which had a dramatic impact on ophthalmology health care. On the one hand, the close contacts physicians need when attending to patients could increase the risk of cross-infection between patients or between health care workers and patients, resulting in infection control to be optimized in ophthalmology practice. On the other hand, the need for timely intervention for patients was driving the development of telemedicine during the pandemic ( 34 , 52 ). Finally, the development of diagnostic technology has driven ophthalmology research toward early screening and disease prevention.

The keywords that were still bursting until 2021 were research trends. The keywords “neural networks,” “pharmacokinetics,” “automated detection,” and “retinal images” in this part of the keyword list were consistent with the hot research directions obtained by keyword clustering. Other keywords that had burst to 2021 could be newly emerging keywords that had not yet had time to be highly cited, were hotspots for research in ophthalmology, and were likely to continue to be of interest for some times to come. Concerning the disciplinary analysis, the analysis of this study revealed that there was extensive cross-collaboration in various basic areas of non-ophthalmology-related research. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

Strengths of the study include a global view of research forces in ophthalmology from a wide range of the literature. Additional study strengths include the revealing of highly cited documents in ophthalmology that provide useful information for researchers. Outcome measures addressed the global research force contributions, research hotspots, and research trends of ophthalmology research, providing an in-depth study of the field of ophthalmology.

Only data from the Web of Science Core Collection database were included in this study, but the Web of Science Core Collection database, as a citation database, already contained comprehensive data on the articles and corresponding citations, which was sufficient for capturing the overall development of the scientific field. In addition, the results of the analysis by the visualization software may include some repetitive and meaningless information. We tried to identify some of the hot topics that were influencing ophthalmology research, so the raw data had been further filtered to remove irrelevant or meaningless words.

In conclusion, this study provided a comprehensive analysis of ophthalmology-related research based on the Web of Science Core Collection database. The hotspots in ophthalmology research were epidemiology, prevention, screening, and treatment of ocular diseases, as well as artificial intelligence and fundus imaging technology and telemedicine. Research trends in ophthalmology research were artificial intelligence, drug development, and fundus diseases. There was an extensive cross-talk of ophthalmology-related research in various basic areas. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

Data availability statement

Author contributions.

ZL and GJ designed the study and provided a critical review for the manuscript. YT and WZ wrote the manuscript. YT, WZ, YZ, BZ, YY, and WL collected and analyzed the data. All authors contributed to the article and approved the submitted version.

This study was supported by the National Natural Science Foundation of China (81873675), the Guangdong Basic and Applied Basic Research Foundation (2022A1515011181), the Teaching Reform Research Program of Sun Yat-sen University (JX3030604024), and the Youth Project of State Key Laboratory of Ophthalmology (2021QN02).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Risk of Corneal Transplant Rejection Following COVID-19 Vaccination: A Systematic Review and Meta-analysis

• ORIGINAL RESEARCH
• Open access
• Published: 16 April 2024

Cite this article

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• Parul Chawla Gupta 1   na1 ,
• Bijaya K. Padhi 2 ,
• Hashem Abu Serhan 3 ,
• Arkadiusz Dziedzic 4 ,
• Mahalaqua Nazli Khatib 5   na1 ,
• Shilpa Gaidhane 6 ,
• Quazi Syed Zahiruddin 7 ,
• Abhay M. Gaidhane 8 ,
• Neelima Kukreti 9 ,
• Sarvesh Rustagi 10 &
• Prakasini Satapathy 11 , 12  

Introduction

The COVID-19 pandemic has initiated an unparalleled global vaccination campaign, raising concerns about the vaccine's effects on various health conditions, including the risk of corneal transplant rejection. This systematic review aimed to identify the relationship between COVID-19 vaccination and rejection of corneal transplant, filling a significant gap in the existing medical literature.

A literature search was performed across multiple databases up to February 12, 2024, to identify studies evaluating the risk of corneal transplant rejection post-COVID-19 vaccination. Eligible studies were original research that reported outcomes of corneal graft rejection following vaccination. Nested Knowledge web software facilitated screening and data extraction. The Newcastle–Ottawa Scale was employed for quality assessment. A meta-analysis was conducted to calculate the aggregated relative risk (RR) utilizing R software version 4.3.

Six studies were included in the qualitative synthesis, with four meeting the criteria for meta-analysis. These studies varied in geographic location, surgical techniques, and types of vaccines used. The pooled RR for corneal transplant rejection following COVID-19 vaccination was 0.816 (95% CI 0.178–1.453), indicating no significant risk of rejection. No statistical heterogeneity was observed among the studies ( I 2  = 0%).

Conclusions

This review and meta-analysis found no significant evidence that COVID-19 vaccination increases the risk of corneal graft rejection. However, the current evidence is insufficient to conclusively determine the vaccine's safety for corneal transplant recipients. These findings underscore the need for additional research to confirm these preliminary results and investigate the long-term effects of COVID-19 vaccination on corneal transplants, aiming to provide evidence-based guidance to healthcare providers and patients.

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During the unparalleled worldwide health emergency induced by the COVID-19 pandemic, vaccination has risen as a cornerstone in the collective effort to control the spread of the SARS-CoV-2 virus and alleviate its profound impact on worldwide public health infrastructure [ 1 , 2 , 3 ]. The swift development, regulatory approval, and extensive administration of COVID-19 vaccines have played an instrumental role in decreasing transmission rates, reducing the severity of cases, and lowering mortality rates attributed to the virus [ 4 ]. As the vaccination initiative has expanded, there has been an escalating interest in discerning the potential ramifications of COVID-19 vaccines on various health conditions and medical interventions, particularly concerning organ transplantation procedures [ 5 , 6 , 7 , 8 ].

Corneal transplantation, or keratoplasty, represents a critical surgical intervention for restoring vision in individuals affected by corneal opacities, dystrophies, or scarring [ 9 , 10 , 11 ]. Despite the procedure's notable success rates, the postoperative period is fraught with the challenge of immune-mediated graft rejection, a significant cause of graft failure and subsequent vision loss [ 11 ]. The introduction of the COVID-19 vaccine has sparked a debate over its potential impact on the incidence or intensity of corneal transplant rejection [ 12 ]. This speculation is rooted in the notion that the vaccine's systemic immune activation could amplify the immune response to the transplanted corneal tissue, thereby elevating the risk of graft rejection [ 13 ].

The importance of addressing this concern is twofold; not only does it bear significance for patient care but it also has implications for the formulation of vaccination guidelines for individuals awaiting or having undergone corneal transplantation. Consequently, there is an urgent need to thoroughly examine the existing evidence to determine the relationship between COVID-19 vaccination and the risk of corneal transplant rejection [ 14 , 15 , 16 , 17 ]. While previous systematic reviews have delved into the characteristics and outcomes of corneal transplant rejection in the context of COVID-19 vaccination [ 18 , 19 ], there still needs to be a gap in the literature regarding a comprehensive assessment of the risk associated with corneal graft rejection post-vaccination.

This systematic review and meta-analysis seeks to bridge this gap by aggregating and scrutinizing data from various studies investigating the occurrence of corneal graft rejection after COVID-19 vaccination. The objective is to determine whether a statistically significant association exists between vaccination and an increased risk of rejection. This endeavor is vital for informing clinical decision-making processes, guiding vaccination policy for transplant recipients, and optimizing patient outcomes during the pandemic. By providing a detailed analysis of the evidence, this review aimed to contribute valuable insights to the body of knowledge on the efficacy and safety profile of COVID-19 vaccines in the context of corneal transplantation, thereby aiding healthcare professionals, policymakers, and patients in navigating the complexities of transplantation and vaccination during these challenging times.

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct the systematic review (Table S1 ). For the processes of screening and extracting data, we employed the Nested-Knowledge (Nested-Knowledge, Saint Paul, MN, USA) software. The protocol has been registered in PROSPERO.

Ethical Approval

Ethical approval is not required since this is a systematic review. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments.

Eligibility Criteria

Studies were eligible for inclusion if they involved individuals who had undergone corneal transplantation and subsequently received a COVID-19 vaccine, reported outcomes specifically related to corneal graft rejection, and fell into the categories of either observational studies or clinical trials. On the other hand, exclusions were made for studies that did not explicitly address the incidence or outcomes of corneal transplant rejection after receiving the COVID-19 vaccine. Narrative reviews, case reports, editorial comments, and studies lacking primary data were also excluded from the analysis. The criteria for inclusion are detailed in Table S2.

Search Strategy

A literature search was performed across multiple electronic databases such as EMBASE, PubMed, and Web of Science, from their inception until February 12, 2024, to identify studies assessing the risk of corneal transplant rejection after receiving the COVID-19 vaccine. The search strategy was crafted to encompass a combination of keywords and MeSH terms related to "corneal transplantation," "keratoplasty," "COVID-19," "SARS-CoV-2," "vaccination," and "graft rejection." No language restrictions were imposed to ensure the inclusion of a wide range of relevant studies. Furthermore, we conducted a manual examination of the reference lists from the identified articles to discover additional studies that met our eligibility criteria. Google Scholar was also manually explored for additional relevant publications to enhance the search comprehensively. The details of the full search strategy are provided in Table S3.

For the screening process, we utilized the advanced capabilities of Nested Knowledge software, a specialized tool for managing and streamlining literature. The screening involved two steps: title and abstract, followed by full-text screening. Two independent reviewers screened the articles. A third reviewer resolved any discrepancies.

Data Extraction and Quality Assessment

Data from the included studies was collected by two separate reviewers using a uniform data collection template. The tagging function of Nested Knowledge software was used for data extraction. Data extracted included study design, participant characteristics, type of COVID-19 vaccine administered, incidence of graft rejection, and outcomes of interest, such as Relative Risk (RR)/Odds Ratio (OR)/Hazard Ratio (HR) for graft rejection. Disagreements among reviewers were settled via discussion, or when required, by seeking the opinion of a third reviewer. The quality assessment of the studies incorporated in this review was performed utilizing the Newcastle–Ottawa Scale (NOS) [ 20 , 21 ].

Statistical Analysis

Meta-analytic techniques were employed to estimate the pooled RR of corneal transplant rejection after receiving the COVID-19 vaccine. Heterogeneity among studies was quantified using the I 2 statistic, with values greater than 50% accounting for substantial heterogeneity [ 22 , 23 ]. A random-effects model (REM) was utilized to accommodate the expected variability among studies. The tau-squared ( τ 2 ) statistic, calculated using the maximum likelihood estimation method, served as an additional measure of heterogeneity [ 24 ]. A p value of less than 0.05 was established as the criterion for statistical significance. The analyses were conducted using the "Meta" and "Metaphor" packages within R statistical software, Version 4.3 [ 20 ].

Search Result

The search across various databases resulted in 229 records initially. Upon the removal of 99 duplicates prior to screening, we proceeded to screen 130 records. Out of these, 115 records were excluded for various reasons [ 25 , 26 ], leaving 15 full-text articles to be assessed for eligibility. Additionally, records identified through citation searching and Google Scholar amounted to seven more articles retrieved for potential inclusion, of which four studies were included. Upon full-text review, 13 articles were excluded from the analysis: two were case reports, and 11 did not report the risk of graft rejection. Consequently, six studies were included in the systematic review. Of these, four studies provided data suitable for meta-analysis (Fig.  1 ).

PRISMA flowchart depicting study selection and screening process

Characteristics of Included Studies

Table 1 presents a summary of the characteristics of the studies included in the analysis. Three studies were from Japan [ 27 , 28 , 29 ], two from Italy [ 8 , 30 ], and one from the USA [ 31 ]. All of the studies employed a retrospective design. The surgical techniques utilized across the studies included conventional penetrating keratoplasty (PK), deep anterior lamellar keratoplasty (DALK), Descemet stripping automated endothelial keratoplasty (DSAEK), Descemet membrane endothelial keratoplasty (DMEK), and mushroom PK. The type of COVID-19 vaccine was not reported in two of the studies. In contrast, one study exclusively involved an mRNA vaccine, and another study included a variety of vaccines such as BNT162b2, Moderna, Pfizer COVID-19 Vaccine, Johnson & Johnson's Janssen Vaccine, AZD1222, and Ad26.COV2.S. The quality assessment of the studies is presented in Table S4.

Risk of Corneal Transplant Rejection After Receiving COVID-19 Vaccine

The transplant rejection risk varied between studies. In Japan, Igarashi et al. (2023) [ 27 ] conducted a retrospective cohort study involving 74 vaccinated patients with an average age of 73.3 years who underwent DMEK. Although the specific type of vaccine was not reported, the study observed an HR of 25.5 (95% CI 2.10–309) for graft rejection over a 474-day follow-up period. From Italy, Busin et al. (2022) [ 30 ] analyzed 785 vaccinated patients with a mean age of 58.6 years. Various surgical techniques were utilized, such as DALK, DMEK, DSAEK, and different forms of PK. Vaccinations included BNT162b2 (79.2%), mRNA-based vaccines (15.7%), AZD1222 (3.2%), and Ad26.COV2.S (1.9%). The study reported an HR of 0.75 (95% CI 0.10–5.52) and an incidence rate ratio (IRR) of 0.56 (95% CI 0.13–2.28) following 60 days after the last vaccine dose. In another retrospective study, Roberts et al. (2023) reported that An IRR of 0.53 with a p value of 0.71 was reported, suggesting no significant association with graft rejection. Fujimoto et al. (2021) presented findings from a Japanese study with 29 patients, averaging 72 years old, who received various corneal transplants, including PK, DSAEK, ALK or DALK, and LT. The BNT162b2 vaccine yielded an OR of 5.57 (95% CI 1.18–25.57) and an RR of 4.47 (95% CI 1.15–18.34) within a 74-day follow-up. In the USA, Culp et al. (2022) included 784 patients in a retrospective study that did not disclose mean age or surgical techniques. The vaccines administered were Moderna, Pfizer COVID-19, or Johnson & Johnson's Janssen Vaccine, with a reported RR of 0.92 (95% CI 0.42–2.01), indicating no significant risk of rejection post-vaccination. Fujimoto et al. (2024) conducted a retrospective cohort study in Japan with an average patient age of 67.95 years. Though the number of vaccinated patients was not detailed, the study utilized PK, DSAEK, DALK, and LT with SARS-CoV-2 mRNA vaccines and reported an RR of 5.74 over an extensive 804-day follow-up.

• Meta-analysis

We performed a meta-analysis of four studies that reported the RR for corneal transplant rejection following COVID-19 vaccination (Fig.  2 ). The studies included in the analysis were Busin 2022, Culp 2022, Fujimoto 2021, and Igarashi 2023. The pooled RR was estimated using a REM, accounting for variation within and between studies. The pooled RR for the meta-analysis was 0.816 (95% CI 0.178–1.453), indicating no significant risk of corneal transplant rejection after receiving COVID-19 vaccine across the included studies. The prediction interval, which provides a range where the true effect is expected to lie in similar studies, was – 0.584 to 2.215. There was no observed heterogeneity among the studies (Tau 2  = 0; Chi 2  = 1.16, ( p  = 0.76); I 2  = 0%).

Forest plot depicting the risk of corneal transplant rejection with COVID-19 vaccination

Publication Bias

Our ability to evaluate the presence of publication bias was constrained by the limited number of studies included in our meta-analysis. Typically, assessment methods such as funnel plots or Egger's test require more studies to provide reliable results. Due to this limitation, we cannot draw definitive conclusions about the potential for publication bias in this body of research.

Our study represents the first attempt to perform a meta-analysis assessing the risk of corneal transplant rejection after COVID-19 vaccine administration. Our analysis revealed a pooled RR of 0.816 (95% CI 0.178–1.453) for corneal transplant rejection after receiving the COVID-19 vaccine. This finding suggests no significant increase in the risk of graft rejection among vaccinated individuals. The studies analyzed varied in geographic location, surgical techniques, and types of COVID-19 vaccines administered, reflecting a broad spectrum of clinical practices and patient experiences. Despite these variations, the overall lack of a significant association between COVID-19 vaccination and increased risk of corneal graft rejection may suggest a favorable safety profile for the vaccines in the context of corneal transplantation. Nonetheless, the available evidence still needs to provide a definitive conclusion.

While previous systematic reviews have broached the subject by collating case reports, our study provides a risk analysis of corneal transplant rejection with COVID-19 vaccination. For instance, a previous review [ 19 ] documented various types of corneal transplants, including 12 cases of penetrating keratoplasty, six of DMEK, four of DSAEK, and one instance of living-related conjunctival-limbal allograft. The onset of graft rejection post-vaccination ranged from as short as 1 day to up to 6 weeks. The leading clinical presentation was corneal edema, occurring in 20 eyes. Other notable symptoms included keratic precipitates and conjunctival or ciliary injection, each observed in 14 eyes. The primary treatment involved the frequent application of topical corticosteroids in 12 eyes and a regimen combining both topical and oral corticosteroids in four eyes. Corneal edema was identified as the most prevalent clinical ocular manifestation post-vaccination, affecting 87% of the cases, with keratic precipitates and conjunctival injection also being significant findings. Another systematic review has also reported similar findings [ 32 ].

Implications of this study are significant for clinical practice, particularly for the management of corneal transplant recipients in the era of COVID-19 vaccination. Our findings suggest that COVID-19 vaccines do not increase the risk of corneal graft rejection, which may alleviate concerns among healthcare providers and patients about the potential impact of vaccination on transplant outcomes. This could potentially support vaccination campaigns within this patient population, emphasizing the importance of protection against COVID-19 without compromising graft survival. Studies with larger cohorts are essential to validate our results and enable a more robust analysis of publication bias. Studies that are longitudinal in nature and include extended periods of follow-up could offer valuable insights into the enduring impacts of COVID-19 vaccination on the survival of corneal grafts. Further research should also aim to include diverse populations to ensure the applicability of the results across different patient demographics. Investigations into the relationship between different types of COVID-19 vaccines and corneal transplant rejection could uncover vaccine-specific risks or benefits. Additionally, mechanistic studies exploring the immunological responses to vaccination in the context of corneal transplantation could further our understanding of graft rejection. Comparative effectiveness research could situate the safety profile of COVID-19 vaccines within the broader context of vaccines administered to transplant recipients. Utilizing data from transplant registries might offer a comprehensive view of the post-vaccination outcomes on a global scale, providing valuable data for healthcare policy and practice. Addressing these research directions will be crucial in advancing our knowledge and ensuring the well-being of corneal transplant recipients during and beyond the pandemic.

Our review is subject to several limitations. The number of available studies that reported an association between the COVID-19 vaccine and corneal transplant rejection was very limited. The inherent biases of retrospective study designs, which constituted all the included studies, may affect the reliability of the results. Additionally, the variability in reporting the type of COVID-19 vaccines and the lack of detailed immunological profiles of the recipients represent gaps in the data. More studies are needed in the future for a better understanding of the condition.

Our analysis has revealed no significant evidence to suggest that COVID-19 vaccination increases the risk of corneal graft rejection. However, the evidence currently available is insufficient to conclusively determine the safety of COVID-19 vaccines on corneal transplant recipients. Further research is necessary to verify these findings and investigate the long-term effects of COVID-19 vaccination on corneal transplant outcomes. This will enable healthcare providers to offer evidence-based advice to their patients.

Data Availability

The data are with the authors and available on request.

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Acknowledgements

The authors acknowledge the Nested-Knowledge, Saint Paul, MN, USA for providing access to the software. Qatar National Library funded the publication of this article.

This author received no funding for conducting the study. The journal’s Rapid Service Fee was funded by Qatar National Library through the author Hashem Abu Serhan.

Author information

Parul Chawla Gupta and Mahalaqua Nazli Khatib contributed equally to this work.

Authors and Affiliations

Department of Ophthalmology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India

Parul Chawla Gupta

Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India

Bijaya K. Padhi

Department of Ophthalmology, Hamad Medical Corporation, Doha, Qatar

Hashem Abu Serhan

Department of Conservative Dentistry with Endodontics, Medical University of Silesia, 40-055, Katowice, Poland

Arkadiusz Dziedzic

Division of Evidence Synthesis, Global Consortium of Public Health and Research, Datta Meghe Institute of Higher Education, Wardha, India

Mahalaqua Nazli Khatib

One Health Centre (COHERD), Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education, Wardha, India

Shilpa Gaidhane

Division of Evidence Synthesis, Global Consortium of Public Health and Research, South Asia Infant Feeding Research Network (SAIFRN), Datta Meghe Institute of Higher Education, Wardha, India

Quazi Syed Zahiruddin

School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, and Global Health Academy, Datta Meghe Institute of Higher Education, Wardha, India

Abhay M. Gaidhane

School of Pharmacy, Graphic Era Hill University, Dehradun, India

Neelima Kukreti

School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand, India

Sarvesh Rustagi

Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

Prakasini Satapathy

Medical Laboratories Techniques Department, AL-Mustaqbal University, Hillah, Babil, 51001, Iraq

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Contributions

Conceptualization: Bijaya K Padhi, Mahalaqua Nazli Khatib, Parul Chawla Gupta; Data curation: Mahalaqua Nazli Khatib, Quazi Syed Zahiruddin, Arkadiusz Dziedzic; Formal analysis: Shilpa Gaidhane, Arkadiusz Dziedzic; Investigation: Shilpa Gaidhane, Prakasini Satapathy, Parul Chawla Gupta; Methodology: Bijaya K Padhi, Arkadiusz Dziedzic, Neelima Kukreti; Project administration: Abhay M Gaidhane; Resources: Quazi Syed Zahiruddin, Prakasini Satapathy; Software: Sarvesh Rustagi; Supervision: Abhay M Gaidhane; Validation: Neelima Kukreti, Sarvesh Rustagi; Visualization: Hashem Abu Serhan; Writing – original draft: Mahalaqua Nazli Khatib; Writing – review & editing: Hashem Abu Serhan, Prakasini Satapathy.

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Correspondence to Hashem Abu Serhan .

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Ethical approval is not required since it is a systematic review. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments.

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Gupta, P.C., Padhi, B.K., Abu Serhan, H. et al. Risk of Corneal Transplant Rejection Following COVID-19 Vaccination: A Systematic Review and Meta-analysis. Ophthalmol Ther (2024). https://doi.org/10.1007/s40123-024-00941-y

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DOI : https://doi.org/10.1007/s40123-024-00941-y

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Ocular Pentastomiasis in Human Hosts

• 1 Department of Ophthalmology, Cliniques Universitaires Saint-Luc, UCLouvain, Bruxelles, Belgium
• 2 Department of Ophthalmology, CHU UCL NAMUR, UCLouvain, Yvoir, Belgium
• 3 National Reference Center for Tropical Diseases, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
• 4 St Raphael Ophthalmological Center, Ophthalmological Ambulance, Mbuji Mayi, Democratic Republic of Congo

Pentastomiasis is a rare zoonotic disease caused by the larval stages of pentastomid parasites. 1 , 2 Most human infections occur in the tropics and subtropics. In Africa and Asia, the disease is often caused by Armillifer species, which have snakes as their final host, whereas Linguatula infections are cosmopolitan, with dogs as final hosts. 1 , 2

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Van Acker G , Ates E , Levecq L , Tappe D , Hardi R. Ocular Pentastomiasis in Human Hosts. JAMA Ophthalmol. Published online April 11, 2024. doi:10.1001/jamaophthalmol.2024.0685

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This paper is in the following e-collection/theme issue:

Published on 12.4.2024 in Vol 26 (2024)

Application of AI in in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Authors of this article:

• Jian Huo 1 * , MSc   ; 
• Yan Yu 2 * , MMS   ; 
• Wei Lin 3 , MMS   ; 
• Anmin Hu 2, 3, 4 , MMS   ; 
• Chaoran Wu 2 , MD, PhD  

1 Boston Intelligent Medical Research Center, Shenzhen United Scheme Technology Company Limited, Boston, MA, United States

2 Department of Anesthesia, Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen Key Medical Discipline, Shenzhen, China

3 Shenzhen United Scheme Technology Company Limited, Shenzhen, China

4 The Second Clinical Medical College, Jinan University, Shenzhen, China

*these authors contributed equally

Corresponding Author:

Chaoran Wu, MD, PhD

Department of Anesthesia

Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology

Shenzhen Key Medical Discipline

No 1017, Dongmen North Road

Shenzhen, 518020

Phone: 86 18100282848

Email: [email protected]

Background: The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps.

Objective: The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images.

Methods: The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots.

Results: A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns.

Conclusions: This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies.

Trial Registration: PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181

Introduction

The definition of pain was revised to “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage” in 2020 [ 1 ]. Acute postoperative pain management is important, as pain intensity and duration are critical influencing factors for the transition of acute pain to chronic postsurgical pain [ 2 ]. To avoid the development of chronic pain, guidelines were promoted and discussed to ensure safe and adequate pain relief for patients, and clinicians were recommended to use a validated pain assessment tool to track patients’ responses [ 3 ]. However, these tools, to some extent, depend on communication between physicians and patients, and continuous data cannot be provided [ 4 ]. The continuous assessment and recording of patient pain intensity will not only reduce caregiver burden but also provide data for chronic pain research. Therefore, automatic and accurate pain measurements are necessary.

Researchers have proposed different approaches to measuring pain intensity. Physiological signals, for example, electroencephalography and electromyography, have been used to estimate pain [ 5 - 7 ]. However, it was reported that current pain assessment from physiological signals has difficulties isolating stress and pain with machine learning techniques, as they share conceptual and physiological similarities [ 8 ]. Recent studies have also investigated pain assessment tools for certain patient subgroups. For example, people with deafness or an intellectual disability may not be able to communicate well with nurses, and an objective pain evaluation would be a better option [ 9 , 10 ]. Measuring pain intensity from patient behaviors, such as facial expressions, is also promising for most patients [ 4 ]. As the most comfortable and convenient method, computer vision techniques require no attachments to patients and can monitor multiple participants using 1 device [ 4 ]. However, pain intensity, which is important for pain research, is often not reported.

With the growing trend of assessing pain intensity using artificial intelligence (AI), it is necessary to summarize current publications to determine the strengths and gaps of current studies. Existing research has reviewed machine learning applications for acute postoperative pain prediction, continuous pain detection, and pain intensity estimation [ 10 - 14 ]. Input modalities, including facial recordings and physiological signals such as electroencephalography and electromyography, were also reviewed [ 5 , 8 ]. There have also been studies focusing on deep learning approaches [ 11 ]. AI was applied in children and infant pain evaluation as well [ 15 , 16 ]. However, no study has focused on pain intensity measurement, and no comparison of test accuracy results has been made.

Current AI applications in pain research can be categorized into 3 types: pain assessment, pain prediction and decision support, and pain self-management [ 14 ]. We consider accurate and automatic pain assessment to be the most important area and the foundation of future pain research. In this study, we performed a systematic review and meta-analysis to assess the diagnostic performance of current publications for multilevel pain evaluation.

This study was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42023418181) and carried out strictly following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 17 ] .

Study Eligibility

Studies that reported AI techniques for multiclass pain intensity classification were eligible. Records including nonhuman or infant participants or 2-class pain detection were excluded. Only studies using facial images of the test participants were accepted. Clinically used pain assessment tools, such as the visual analog scale (VAS) and numerical rating scale (NRS), and other pain intensity indicators, were rejected in the meta-analysis. Textbox 1 presents the eligibility criteria.

Study characteristics and inclusion criteria

• Participants: children and adults aged 12 months or older
• Setting: no restrictions
• Index test: artificial intelligence models that measure pain intensity from facial images
• Reference standard: no restrictions for systematic review; Prkachin and Solomon pain intensity score for meta-analysis
• Study design: no need to specify

Study characteristics and exclusion criteria

• Participants: infants aged 12 months or younger and animal subjects
• Setting: no need to specify
• Index test: studies that use other information such as physiological signals
• Reference standard: other pain evaluation tools, e.g., NRS, VAS, were excluded from meta-analysis
• Study design: reviews

Report characteristics and inclusion criteria

• Year: published between January 1, 2012, and September 30, 2023
• Language: English only
• Publication status: published
• Test accuracy metrics: no restrictions for systematic reviews; studies that reported contingency tables were included for meta-analysis

Report characteristics and exclusion criteria

• Year: no need to specify
• Language: no need to specify
• Publication status: preprints not accepted
• Test accuracy metrics: studies that reported insufficient metrics were excluded from meta-analysis

Search Strategy

In this systematic review, databases including PubMed, Embase, IEEE, Web of Science, and the Cochrane Library were searched until December 2022, and no restrictions were applied. Keywords were “artificial intelligence” AND “pain recognition.” Multimedia Appendix 1 shows the detailed search strategy.

Data Extraction

A total of 2 viewers screened titles and abstracts and selected eligible records independently to assess eligibility, and disagreements were solved by discussion with a third collaborator. A consentient data extraction sheet was prespecified and used to summarize study characteristics independently. Table S5 in Multimedia Appendix 1 shows the detailed items and explanations for data extraction. Diagnostic accuracy data were extracted into contingency tables, including true positives, false positives, false negatives, and true negatives. The data were used to calculate the pooled diagnostic performance of the different models. Some studies included multiple models, and these models were considered independent of each other.

Study Quality Assessment

All included studies were independently assessed by 2 viewers using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool [ 18 ]. QUADAS-2 assesses bias risk across 4 domains, which are patient selection, index test, reference standard, and flow and timing. The first 3 domains are also assessed for applicability concerns. In the systematic review, a specific extension of QUADAS-2, namely, QUADAS-AI, was used to specify the signaling questions [ 19 ].

Meta-Analysis

Meta-analyses were conducted between different AI models. Models with different algorithms or training data were considered different. To evaluate the performance differences between models, the contingency tables during model validation were extracted. Studies that did not report enough diagnostic accuracy data were excluded from meta-analysis.

Hierarchical summary receiver operating characteristic (SROC) curves were fitted to evaluate the diagnostic performance of AI models. These curves were plotted with 95% CIs and prediction regions around averaged sensitivity, specificity, and area under the curve estimates. Heterogeneity was assessed visually by forest plots. A funnel plot was constructed to evaluate the risk of bias.

Subgroup meta-analyses were conducted to evaluate the performance differences at both the model level and task level, and subgroups were created based on different tasks and the proportion of positive and negative samples.

All statistical analyses and plots were produced using RStudio (version 4.2.2; R Core Team) and the R package meta4diag (version 2.1.1; Guo J and Riebler A) [ 20 ].

Study Selection and Included Study Characteristics

A flow diagram representing the study selection process is shown in ( Figure 1 ). After removing 1039 duplicates, the titles and abstracts of a total of 5653 papers were screened, and the percentage agreement of title or abstract screening was 97%. After screening, 51 full-text reports were assessed for eligibility, among which 45 reports were included in the systematic review [ 21 - 65 ]. The percentage agreement of the full-text review was 87%. In 40 of the included studies, contingency tables could not be made. Meta-analyses were conducted based on 8 AI models extracted from 6 studies. Individual study characteristics included in the systematic review are provided in Tables 1 and 2 . The facial feature extraction method can be categorized into 2 classes: geometrical features (GFs) and deep features (DFs). One typical method of extracting GFs is to calculate the distance between facial landmarks. DFs are usually extracted by convolution operations. A total of 20 studies included temporal information, but most of them (18) extracted temporal information through the 3D convolution of video sequences. Feature transformation was also commonly applied to reduce the time for training or fuse features extracted by different methods before inputting them into the classifier. For classifiers, support vector machines (SVMs) and convolutional neural networks (CNNs) were mostly used. Table 1 presents the model designs of the included studies.

a No temporal features are shown by – symbol, time information extracted from 2 images at different time by +, and deep temporal features extracted through the convolution of video sequences by ++.

b SVM: support vector machine.

c GF: geometric feature.

d GMM: gaussian mixture model.

e TPS: thin plate spline.

f DML: distance metric learning.

g MDML: multiview distance metric learning.

h AAM: active appearance model.

i RVR: relevance vector regressor.

j PSPI: Prkachin and Solomon pain intensity.

k I-FES: individual facial expressiveness score.

l LSTM: long short-term memory.

m HCRF: hidden conditional random field.

n GLMM: generalized linear mixed model.

o VLAD: vector of locally aggregated descriptor.

p SVR: support vector regression.

q MDS: multidimensional scaling.

r ELM: extreme learning machine.

s Labeled to distinguish different architectures of ensembled deep learning models.

t DCNN: deep convolutional neural network.

u GSM: gaussian scale mixture.

v DOML: distance ordering metric learning.

w LIAN: locality and identity aware network.

x BiLSTM: bidirectional long short-term memory.

a UNBC: University of Northern British Columbia-McMaster shoulder pain expression archive database.

b LOSO: leave one subject out cross-validation.

c ICC: intraclass correlation coefficient.

d CT: contingency table.

e AUC: area under the curve.

f MSE: mean standard error.

g PCC: Pearson correlation coefficient.

h RMSE: root mean standard error.

i MAE: mean absolute error.

j ICC: intraclass coefficient.

k CCC: concordance correlation coefficient.

l Reported both external and internal validation results and summarized as intervals.

Table 2 summarizes the characteristics of model training and validation. Most studies used publicly available databases, for example, the University of Northern British Columbia-McMaster shoulder pain expression archive database [ 57 ]. Table S4 in Multimedia Appendix 1 summarizes the public databases. A total of 7 studies used self-prepared databases. Frames from video sequences were the most used test objects, as 37 studies output frame-level pain intensity, while few measure pain intensity from video sequences or photos. It was common that a study redefined pain levels to have fewer classes than ground-truth labels. For model validation, cross-validation and leave-one-subject-out validation were commonly used. Only 3 studies performed external validation. For reporting test accuracies, different evaluation metrics were used, including sensitivity, specificity, mean absolute error (MAE), mean standard error (MSE), Pearson correlation coefficient (PCC), and intraclass coefficient (ICC).

Methodological Quality of Included Studies

Table S2 in Multimedia Appendix 1 presents the study quality summary, as assessed by QUADAS-2. There was a risk of bias in all studies, specifically in terms of patient selection, caused by 2 issues. First, the training data are highly imbalanced, and any method to adjust the data distribution may introduce bias. Next, the QUADAS-AI correspondence letter [ 19 ] specifies that preprocessing of images that changes the image size or resolution may introduce bias. However, the applicability concern is low, as the images properly represent the feeling of pain. Studies that used cross-fold validation or leave-one-out cross-validation were considered to have a low risk of bias. Although the Prkachin and Solomon pain intensity (PSPI) score was used by most of the studies, its ability to represent individual pain levels was not clinically validated; as such, the risk of bias and applicability concerns were considered high when the PSPI score was used as the index test. As an advantage of computer vision techniques, the time interval between the index tests was short and was assessed as having a low risk of bias. Risk proportions are shown in Figure 2 . For all 315 entries, 39% (124) were assessed as high-risk. In total, 5 studies had the lowest risk of bias, with 6 domains assessed as low risk [ 26 , 27 , 31 , 32 , 59 ].

Pooled Performance of Included Models

In 6 studies included in the meta-analysis, there were 8 different models. The characteristics of these models are summarized in Table S1 in Multimedia Appendix 2 [ 23 , 24 , 26 , 32 , 41 , 57 ]. Classification of PSPI scores greater than 0, 2, 3, 6, and 9 was selected and considered as different tasks to create contingency tables. The test performance is shown in Figure 3 as hierarchical SROC curves; 27 contingency tables were extracted from 8 models. The sensitivity, specificity, and LDOR were calculated, and the combined sensitivity was 98% (95% CI 96%-99%), the specificity was 98% (95% CI 97%-99%), the LDOR was 7.99 (95% CI 6.73-9.31) and the AUC was 0.99 (95% CI 0.99-1).

Subgroup Analysis

In this study, subgroup analysis was conducted to investigate the performance differences within models. A total of 8 models were separated and summarized as a forest plot in Multimedia Appendix 3 [ 23 , 24 , 26 , 32 , 41 , 57 ]. For model 1, the pooled sensitivity, specificity, and LDOR were 95% (95% CI 86%-99%), 99% (95% CI 98%-100%), and 8.38 (95% CI 6.09-11.19), respectively. For model 2, the pooled sensitivity, specificity, and LDOR were 94% (95% CI 84%-99%), 95% (95% CI 88%-99%), and 6.23 (95% CI 3.52-9.04), respectively. For model 3, the pooled sensitivity, specificity, and LDOR were 100% (95% CI 99%-100%), 100% (95% CI 99%-100%), and 11.55% (95% CI 8.82-14.43), respectively. For model 4, the pooled sensitivity, specificity, and LDOR were 83% (95% CI 43%-99%), 94% (95% CI 79%-99%), and 5.14 (95% CI 0.93-9.31), respectively. For model 5, the pooled sensitivity, specificity, and LDOR were 92% (95% CI 68%-99%), 94% (95% CI 78%-99%), and 6.12 (95% CI 1.82-10.16), respectively. For model 6, the pooled sensitivity, specificity, and LDOR were 94% (95% CI 74%-100%), 94% (95% CI 78%-99%), and 6.59 (95% CI 2.21-11.13), respectively. For model 7, the pooled sensitivity, specificity, and LDOR were 98% (95% CI 90%-100%), 97% (95% CI 87%-100%), and 8.31 (95% CI 4.3-12.29), respectively. For model 8, the pooled sensitivity, specificity, and LDOR were 98% (95% CI 93%-100%), 97% (95% CI 88%-100%), and 8.65 (95% CI 4.84-12.67), respectively.

Heterogeneity Analysis

The meta-analysis results indicated that AI models are applicable for estimating pain intensity from facial images. However, extreme heterogeneity existed within the models except for models 3 and 5, which were proposed by Rathee and Ganotra [ 24 ] and Semwal and Londhe [ 32 ]. A funnel plot is presented in Figure 4 . A high risk of bias was observed.

Pain management has long been a critical problem in clinical practice, and the use of AI may be a solution. For acute pain management, automatic measurement of pain can reduce the burden on caregivers and provide timely warnings. For chronic pain management, as specified by Glare et al [ 2 ], further research is needed, and measurements of pain presence, intensity, and quality are one of the issues to be solved for chronic pain studies. Computer vision could improve pain monitoring through real-time detection for clinical use and data recording for prospective pain studies. To our knowledge, this is the first meta-analysis dedicated to AI performance in multilevel pain level classification.

In this study, one model’s performance at specific pain levels was described by stacking multiple classes into one to make each task a binary classification problem. After careful selection in both the medical and engineering databases, we observed promising results of AI in evaluating multilevel pain intensity through facial images, with high sensitivity (98%), specificity (98%), LDOR (7.99), and AUC (0.99). It is reasonable to believe that AI can accurately evaluate pain intensity from facial images. Moreover, the study quality and risk of bias were evaluated using an adapted QUADAS-2 assessment tool, which is a strength of this study.

To investigate the source of heterogeneity, it was assumed that a well-designed model should have familiar size effects regarding different levels, and a subgroup meta-analysis was conducted. The funnel and forest plots exhibited extreme heterogeneity. The model’s performance at specific pain levels was described and summarized by a forest plot. Within-model heterogeneity was observed in Multimedia Appendix 3 [ 23 , 24 , 26 , 32 , 41 , 57 ] except for 2 models. Models 3 and 5 were different in many aspects, including their algorithms and validation methods, but were both trained with a relatively small data set, and the proportion of positive and negative classes was relatively close to 1. Because training with imbalanced data is a critical problem in computer vision studies [ 66 ], for example, in the University of Northern British Columbia-McMaster pain data set, fewer than 10 frames out of 48,398 had a PSPI score greater than 13. Here, we emphasized that imbalanced data sets are one major cause of heterogeneity, resulting in the poorer performance of AI algorithms.

We tentatively propose a method to minimize the effect of training with imbalanced data by stacking multiple classes into one class, which is already presented in studies included in the systematic review [ 26 , 32 , 42 , 57 ]. Common methods to minimize bias include resampling and data augmentation [ 66 ]. This proposed method is used in the meta-analysis to compare the test results of different studies as well. The stacking method is available when classes are only different in intensity. A disadvantage of combined classes is that the model would be insufficient in clinical practice when the number of classes is low. Commonly used pain evaluation tools, such as VAS, have 10 discrete levels. It is recommended that future studies set the number of pain levels to be at least 10 for model training.

This study is limited for several reasons. First, insufficient data were included because different performance metrics (mean standard error and mean average error) were used in most studies, which could not be summarized into a contingency table. To create a contingency table that can be included in a meta-analysis, the study should report the following: the number of objects used in each pain class for model validation, and the accuracy, sensitivity, specificity, and F 1 -score for each pain class. This table cannot be created if a study reports the MAE, PCC, and other commonly used metrics in AI development. Second, a small study effect was observed in the funnel plot, and the heterogeneity could not be minimized. Another limitation is that the PSPI score is not clinically validated and is not the only tool that assesses pain from facial expressions. There are other clinically validated pain intensity assessment methods, such as the Faces Pain Scale-revised, Wong-Baker Faces Pain Rating Scale, and Oucher Scale [ 3 ]. More databases could be created based on the above-mentioned tools. Finally, AI-assisted pain assessments were supposed to cover larger populations, including incommunicable patients, for example, patients with dementia or patients with masked faces. However, only 1 study considered patients with dementia, which was also caused by limited databases [ 50 ].

AI is a promising tool that can help in pain research in the future. In this systematic review and meta-analysis, one approach using computer vision was investigated to measure pain intensity from facial images. Despite some risk of bias and applicability concerns, CV models can achieve excellent test accuracy. Finally, more CV studies in pain estimation, reporting accuracy in contingency tables, and more pain databases are encouraged for future studies. Specifically, the creation of a balanced public database that contains not only healthy but also nonhealthy participants should be prioritized. The recording process would be better in a clinical environment. Then, it is recommended that researchers report the validation results in terms of accuracy, sensitivity, specificity, or contingency tables, as well as the number of objects for each pain class, for the inclusion of a meta-analysis.

Acknowledgments

WL, AH, and CW contributed to the literature search and data extraction. JH and YY wrote the first draft of the manuscript. All authors contributed to the conception and design of the study, the risk of bias evaluation, data analysis and interpretation, and contributed to and approved the final version of the manuscript.

Data Availability

The data sets generated during and analyzed during this study are available in the Figshare repository [ 67 ].

Conflicts of Interest

None declared.

PRISMA checklist, risk of bias summary, search strategy, database summary and reported items and explanations.

Study performance summary.

Forest plot presenting pooled performance of subgroups in meta-analysis.

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Abbreviations

Edited by A Mavragani; submitted 26.07.23; peer-reviewed by M Arab-Zozani, M Zhang; comments to author 18.09.23; revised version received 08.10.23; accepted 28.02.24; published 12.04.24.

©Jian Huo, Yan Yu, Wei Lin, Anmin Hu, Chaoran Wu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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