Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Zhao, Junqiang; Lü, Yi; Qian, Yong; Luo, Yuxin; Yang, Weihua

doi:10.2196/37532

Cited by 25 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This trend coincided with the increased availability of computing resources and the increasing popularity of AI solutions in other areas of healthcare. It also highlights the fact that most clinical trials involving AI for cancer diagnosis began after 2017 [25,104]. Interestingly, this trend was also observed in certain oncology subspecialties.…”

Section: General Informationmentioning

confidence: 73%

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study

Wu¹,

Yu²,

Zhang³

et al. 2022

Front. Biosci. (Landmark Ed)

View full text Add to dashboard Cite

Background:The past decade has seen major advances in the use of artificial intelligence (AI) to solve various biomedical problems, including cancer. This has resulted in more than 6000 scientific papers focusing on AI in oncology alone. The expansiveness of this research area presents a challenge to those seeking to understand how it has developed. A scientific analysis of AI in the oncology literature is therefore crucial for understanding its overall structure and development. This may be addressed through bibliometric analysis, which employs computational and visual tools to identify research activity, relationships, and expertise within large collections of bibliographic data. There is already a large volume of research data regarding the development of AI applications in cancer research. However, there is no published bibliometric analysis of this topic that offers comprehensive insights into publication growth, co-citation networks, research collaboration, and keyword co-occurrence analysis for technological trends involving AI across the entire spectrum of oncology research. The purpose of this study is to investigate documents published during the last decade using bibliometric indicators and network visualization. This will provide a detailed assessment of global research activities, key themes, and AI trends over the entire breadth of the oncology field. It will also specifically highlight top-performing authors, organizations, and nations that have made major contributions to this research domain, as well as their interactions via network collaboration maps and betweenness centrality metric. This study represents the first global investigation of AI covering the entire cancer field and using several validated bibliometric techniques. It should provide valuable reference material for reorienting this field and for identifying research trajectories, topics, major publications, and influential entities including scholars, institutions, and countries. It will also identify international collaborations at three levels: micro (that of an individual researcher), meso (that of an institution), and macro (that of a country), in order to inform future lines of research. Methods: The Science Citation Index Expanded from the Web of Science Core Collection was searched for articles and reviews pertaining exclusively to AI in cancer from 2012 through 2022. Annual publication trends were plotted using Microsoft Excel 2019. CiteSpace and VOSViewer were used to investigate the most productive countries, researchers, journals, as well as the sharing of resources, intellectual property, and knowledge base in this field, along with the co-citation analysis of references and keywords. Results: A total of 6757 documents were retrieved. China produced the most publications of any country (2087, 30.89%), and Sun Yat Sen University the highest number (167, 2.47%) of any institute. WEI WANG was the most prolific author (33, 0.49%). RUI ZHANG ranked first for highest betweenness centrality (0.21) and collaboration criteria. Sci...

show abstract

Section: General Informationmentioning

confidence: 73%

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study

Wu¹,

Yu²,

Zhang³

et al. 2022

Front. Biosci. (Landmark Ed)

View full text Add to dashboard Cite

show abstract

“…Bibliometric analysis is a method of statistically evaluating the research status and trends, as well as the most influential studies in a specific field, and has been successfully applied to relevant research in medicine ( 18 , 19 ). Citation analysis is one of the main methods of bibliometrics, which can evaluate the quality and recognition of articles, and better understand the discipline construction and development of a research field.…”

Section: Introductionmentioning

confidence: 99%

Global research trends on the links between gut microbiota and cancer immunotherapy: A bibliometric analysis (2012-2021)

Yang

Zhao

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

BackgroundThere is a crosstalk between gut microbiota (GM) and cancer immunotherapy (CI). The purpose of this study is to use bibliometric analysis to identify the highly cited papers relating to GM/CI and explore the research status and development trends of the GM/CI research.MethodsA literature search regarding GM/CI publications from 2012 to 2021 was undertaken on July 4, 2022. The article titles, journals, authors, institutions, countries, total citations, keywords, and other information were extracted from the Science Citation Index Expanded (SCIE) of Web of Science Core Collection (WoSCC). The Bibliometrix of R package and VOSviewer were used for bibliometric analysis.ResultsA total of 665 papers were extracted. The number of papers has increased rapidly over the past decade, especially after 2018. The United States and China had the most publications and made great contributions to this field. Th5e Univ Texas MD Anderson Canc Ctr and Univ Paris Saclay were absolutely in the leading position in GM/CI. The most influential authors were Zitvogel L and Routy B. Frontiers in Immunology had the most publications and Science had the most total citations. Historical direct citation analysis explained the historical evolution in GM/CI. Highly cited papers and high-frequency keywords illustrated the current status and trends of GM/CI. Four clusters were identified and the important topics included the role of GM and antibiotics in CI, the methods of targeting GM to improve CI outcomes, the mechanism by which GM affects CI and the application of ICIs in melanoma. “Tumor microbiome”, “proton pump inhibitors” and “prognosis” may be the new focus of attention in the next few years.ConclusionThis study filtered global publications on GM/CI correlation and analyzed their bibliometric characteristics, identified the most cited papers in GM/CI, and gained insight into the status, hotspots and trends of global GM/CI research, which may inform researchers and practitioners of future directions.

show abstract

“…The number of patients suffering from retinal illness has increased dramatically in recent years 1 , 2 . Age-related macular degeneration (AMD) and diabetic macular edema (DME) are two frequent retinal disorders that can lead to lifelong blindness.…”

Section: Introductionmentioning

confidence: 99%

An interpretable transformer network for the retinal disease classification using optical coherence tomography

Wang

Han

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models’ decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.

show abstract

Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study

Cited by 25 publications

References 46 publications

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study

Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study

Global research trends on the links between gut microbiota and cancer immunotherapy: A bibliometric analysis (2012-2021)

An interpretable transformer network for the retinal disease classification using optical coherence tomography

Contact Info

Product

Resources

About