A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Worldwide productivity and research trend of publications concerning intestinal polyps: A bibliometric study

There is a significant relationship between intestinal polyps and colorectal cancer, and in recent years, research on intestinal polyps has been rapidly developing around the world. However, there is still a lack of adequate quantification and analysis of publications in this field. The aim of this study was to perform a comprehensive bibliometric analysis of publications related to intestinal polyps over the past 20 years. To enhance the understanding of current research hotspots and potential trends, and to point out the direction of future research. Publications related to intestinal polyps were retrieved from the Science Citation Index Expanded in Web of Science Core Collection. the Bibliometric online analysis platform (https://bibliometric.com/app), the Bibliometrix Package, and the CiteSpace are used for bibliometric analysis and visualization, including the overall range of annual output and annual citations, country-region analysis, author and institution analysis, core journal analysis, reference and keyword analysis. Prior to 2017, the amount of research on intestinal polyps was slow to grow, but it picked up speed after that year. In 1019 journals, 4280 papers on intestinal polyps were published in English. The journal with the highest productivity was Gastrointestinal Endoscopy (189, 4.42%). United States (1124, 26.26%), which is also the hub of collaboration in this subject, was the most productive nation. Mayo Clinic (n = 70, 1.64%) is the most productive institution. Intestinal microbiota, endoscopic mucosal resection, gut microbiota, deep learning, tea polyphenol, insulin resistance and artificial intelligence were current hot subjects in the field. Studies of intestinal polyps increased significantly after 2017. The United States contributed the largest number of publications. Countries and institutions were actively cooperating with one another. artificial intelligence is currently an emerging topic.

A bibliometric analysis of insomnia in adolescent

Background

The negative effects of insomnia on adolescents' development, academic performance, and quality of life place a burden on families, schools, and society. As one of the most important research directions for insomnia, adolescent insomnia has significant research value, social value, and practical significance. Unfortunately, there is no bibliometric analysis in this field of study. This study aims to analyze published articles using bibliometrics, summarize the current research progress and hot topics in this field systematically and exhaustively, and predict the future direction and trend of research.

Methods

For this study, the Web of Science Core Collection (WoSCC) database was searched between 2002 and 2022 for publications related to adolescent insomnia. The R-bibliometrix, VOSViewer, and CiteSpace software were utilized for bibliometric analysis.

Results

This investigation included 2468 publications from 3102 institutions in 87 countries, led by China and the United States. This field of research has entered a period of rapid development since 2017. The journal with the most publications on adolescent insomnia is Sleep, which is also the most co-cited journal. American Journal of Psychology has the highest impact factor among the top 10 journals. These papers were written by 10605 authors; notably, Liu Xianchen emerged as the author with the highest frequency of publications, while Mary A. Carskadon was the most frequently co-cited author. Mental health and comorbid diseases were the main research directions in this field. "Depression," "anxiety," "mental health," "COVID-19," "stress," "quality of life," "heart rate variability," and "attention-deficit hyperactivity disorder" were hot spots and trends in this field at the current moment.

Conclusion

The research on adolescent insomnia has social value, research value, and research potential; its development is accelerating, and an increasing number of researchers are focusing on it. This study summarized and analyzed the development process, hot spots, and trends of adolescent insomnia research using bibliometric analysis, which identified the current hot topics in this field and predicted the development trend for the future.

Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm

INTRODUCTION

Rotator cuff tear (RCT) is a challenging and common musculoskeletal disease. Magnetic resonance imaging (MRI) is a commonly used diagnostic modality for RCT, but the interpretation of the results is tedious and has some reliability issues. In this study, we aimed to evaluate the accuracy and efficacy of the 3-dimensional (3D) MRI segmentation for RCT using a deep learning algorithm.

METHODS

A 3D U-Net convolutional neural network (CNN) was developed to detect, segment, and visualize RCT lesions in 3D, using MRI data from 303 patients with RCTs. The RCT lesions were labeled by two shoulder specialists in the entire MR image using in-house developed software. The MRI-based 3D U-Net CNN was trained after the augmentation of a training dataset and tested using randomly selected test data (training: validation: test data ratio was 6:2:2). The segmented RCT lesion was visualized in a three-dimensional reconstructed image, and the performance of the 3D U-Net CNN was evaluated using the Dice coefficient, sensitivity, specificity, precision, F1-score, and Youden index.

RESULTS

A deep learning algorithm using a 3D U-Net CNN successfully detected, segmented, and visualized the area of RCT in 3D. The model's performance reached a 94.3% of Dice coefficient score, 97.1% of sensitivity, 95.0% of specificity, 84.9% of precision, 90.5% of F1-score, and Youden index of 91.8%.

CONCLUSION

The proposed model for 3D segmentation of RCT lesions using MRI data showed overall high accuracy and successful 3D visualization. Further studies are necessary to determine the feasibility of its clinical application and whether its use could improve care and outcomes.

Knowledge domain and hotspots concerning photosensitive hydrogels for tissue engineering applications: A bibliometric and visualized analysis (1996-2022)

Objective: The aim of tissue engineering (TE) is to replace the damaged tissues or failed organs, or restore their missing functions. The important means to achieve this aim is to integrate biomaterials and life elements. Hydrogels are very attractive biomaterials in the field of TE. In particular, engineering extracellular matrices (ECMs) formed by photosensitive hydrogels have captivated much attention, because photopolymerization has many advantages over traditional polymerization approaches, such as rapidity of reaction, spatiotemporal controllability of polymerization process, and operability at physiological temperature, especially it can realize the fabrications of engineering ECMs in the presence of living cells. There have been many excellent reviews on the applications of photosensitive hydrogels in TE in recent years, however, it is inevitable that researchers may have left out many important facts due to exploring the literature from one or a few aspects. It is also a great challenge for researchers to explore the internal relationships among countries, institutions, authors, and references from a large number of literatures in related fields. Therefore, bibliometrics may be a powerful tool to solve the above problems. A bibliometric and visualized analysis of publications concerning the photosensitive hydrogels for TE applications was performed, and the knowledge domain, research hotspots and frontiers in this topic were identified according to the analysis results.

Methods: We identified and retrieved the publications regarding the photosensitive hydrogels for TE applications between 1996 and 2022 from Web of Science Core Collection (WoSCC). Bibliometric and visualized analysis employing CiteSpace software and R-language package Bibliometrix were performed in this study.

Results: 778 publications meeting the eligibility criteria were identified and retrieved from WoSCC. Among those, 2844 authors worldwide participated in the studies in this field, accompanied by an average annual article growth rate of 15.35%. The articles were co-authored by 800 institutions from 46 countries/regions, and the United States published the most, followed by China and South Korea. As the two countries that published the most papers, the United States and China could further strengthen cooperation in this field. Univ Colorado published the most articles (n = 150), accounting for 19.28% of the total. The articles were distributed in 112 journals, among which Biomaterials (n = 66) published the most articles, followed by Acta Biomaterialia (n = 54) and Journal of Biomedical Materials Research Part A (n = 42). The top 10 journals published 47.8% of the 778 articles. The most prolific author was Anseth K (n = 33), followed by Khademhosseini A (n = 29) and Bryant S (n = 22). A total of 1443 keywords were extracted from the 778 articles and the keyword with the highest centrality was “extracellular matrix” (centrality: 0.12). The keywords appeared recently with strong citation bursts were “gelatin”, “3d printing” and “3d bioprinting”, representing the current research hotspots in this field. “Gelma”, “3d printing” and “thiol-ene” were the research frontiers in recent years.

Conclusion: This bibliometric and visualized study offered a comprehensive understanding of publications regarding the photosensitive hydrogels for TE applications from 1996 to 2022, including the knowledge domain, research hotspots and frontiers in this filed. The outcome of this study would provide insights for scholars in the related research filed.

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis

PURPOSE

Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.

METHODS

This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.

RESULTS

The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers.

CONCLUSIONS

Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.