Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Zhao, Junqiang; Lu, Yi; Zhu, Shaojun; Li, Keran; Jiang, Qin; Yang, Weihua

doi:10.3389/fphar.2022.930520

Cited by 8 publications

(7 citation statements)

References 66 publications

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“…[37] The burst keywords in the different periods represented different research hotspots and frontiers. [38]…”

Section: Discussionmentioning

confidence: 99%

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Insights into adeno-associated virus-based ocular gene therapy: A bibliometric and visual analysis

Chen

Nie

et al. 2023

Medicine

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Background: Adeno-associated virus (AAV) plays a vital role in ocular gene therapy and has been widely studied since 1996. This study summarizes and explores the publication outputs and future research trends of AAV-based ocular gene therapy. Methods: Publications and data about AAV-based ocular gene therapy were downloaded from the Web of Science Core Collection or ClinicalTrials.gov database. The publications and data were analyzed by Microsoft Excel, CiteSpace, VOS viewer, and a free online platform (http://bibliometric.com). Results: Totally 832 publications from the Web of Science Core Collection relevant to AAV-based ocular gene therapy were published from 1996 to 2022. These publications were contributed by research institutes from 42 countries or regions. The US contributed the most publications among these countries or regions, notably the University of Florida. Hauswirth WW was the most productive author. “Efficacy” and “safety” are the main focus areas for future research according to the references and keywords analysis. Eighty clinical trials examined AAV-based ocular gene therapy were registered on ClinicalTrials.Gov. Institutes from the US and European did the dominant number or the large proportion of the trials. Conclusions: The research focus of the AAV-based ocular gene therapy has transitioned from the study in biological theory to clinical trialing. The AAV-based gene therapy is not limited to inherited retinal diseases but various ocular diseases.

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“…[37] The burst keywords in the different periods represented different research hotspots and frontiers. [38]…”

Section: Discussionmentioning

confidence: 99%

“…[ 37 ] The burst keywords in the different periods represented different research hotspots and frontiers. [ 38 ] The top 10 keywords in the strongest citation bursts were listed in Figure 7 B based on the outbreak year. The first detected burst keyword was “in vivo” in 1999.…”

Section: Discussionmentioning

confidence: 99%

Insights into adeno-associated virus-based ocular gene therapy: A bibliometric and visual analysis

Chen

Nie

et al. 2023

Medicine

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“…It has made significant contributions to the study of diseases such as diabetic retinopathy (DR) [13] , age-related macular degeneration (AMD) [14] , glaucoma [15][16] , and retinopathy of prematurity (ROP) [17] . In addition, AI also includes the detection of genes related to ophthalmic diseases, ocular metabolites, and pathology ocular metabolites [18] . The research on AI demonstrates a diversification of diseases and an in-depth exploration, which holds great significance for disease screening, diagnosis, treatment, and prediction (Figure 1).…”

Section: Overview Of Artificial Intelligence Technologymentioning

confidence: 99%

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

Ying,

Zhang,

et al. 2024

Int J Ophthalmol

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With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

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“…The segmentation of retinal vessels is an essential foundation for the quantitative analysis of fundus images. Since manual segmentation is time-consuming, labor-intensive, and relies on professionals’ subjective judgment, many researchers have turned to computer-aided intervention to achieve automatic retinal vessel segmentation ( Zhao et al, 2022a ; Zhao et al, 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation

Gao

et al. 2023

Front. Cell Dev. Biol.

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Accurate retinal vessel segmentation from fundus images is essential for eye disease diagnosis. Many deep learning methods have shown great performance in this task but still struggle with limited annotated data. To alleviate this issue, we propose an Attention-Guided Cascaded Network (AGC-Net) that learns more valuable vessel features from a few fundus images. Attention-guided cascaded network consists of two stages: the coarse stage produces a rough vessel prediction map from the fundus image, and the fine stage refines the missing vessel details from this map. In attention-guided cascaded network, we incorporate an inter-stage attention module (ISAM) to cascade the backbone of these two stages, which helps the fine stage focus on vessel regions for better refinement. We also propose Pixel-Importance-Balance Loss (PIB Loss) to train the model, which avoids gradient domination by non-vascular pixels during backpropagation. We evaluate our methods on two mainstream fundus image datasets (i.e., DRIVE and CHASE-DB1) and achieve AUCs of 0.9882 and 0.9914, respectively. Experimental results show that our method outperforms other state-of-the-art methods in performance.

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Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Cited by 8 publications

References 66 publications

Insights into adeno-associated virus-based ocular gene therapy: A bibliometric and visual analysis

Insights into adeno-associated virus-based ocular gene therapy: A bibliometric and visual analysis

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation

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