In this study, we aim to evaluate the global scientific production of stem cell research for the past 16 years and provide insights into the characteristics of the stem cell research activities and identify patterns, tendencies, or regularities that may exist in the papers. Data are based on the online version of SCI, Web of Science from 1991 to 2006. Articles referring to stem cell were assessed by many aspects including exponential fitting the trend of publication outputs during 1991–2006, distribution of source title, author keyword, and keyword plus analysis. Based on the exponential fitting the yearly publicans of the last decade, it can also be calculated that, in 2,011, the number of scientific papers on the topic of stem-cell will be twice of the number of publications in 2006. Synthetically analyzing three kinds of keywords, it can be concluded that application of stem cell transplantation technology to human disease therapy, especially research related on “embryonic stem cell” and “mesenchymal stem cell” is the orientation of all the stem cell research in the 21st century. This new bibliometric method can help relevant researchers realize the panorama of global stem cell research, and establish the further research direction.

http://dns2.asia.edu.tw/~ysho/YSHO-English/Publications/PDF/Scientometrics80,%2039.pdf

Global stem cell research trend: Bibliometric analysis as a tool for mapping of trends from 1991 to 2006

Mapping of drinking water research: a bibliometric analysis of research output during 1992-2011.

https://trend.asia.edu.tw/Publications/PDF/Glo%20Pla%20Cha77,%2013.pdf

Trends in research on global climate change: A Science Citation Index Expanded-based analysis

This study was to explore a bibliometric approach to quantitatively assessing current research trends on atmospheric aerosol, using the related literature in the Science Citation Index (SCI) database from 1991 to 2006. Articles were concentrated on the analysis by scientific output, research performances by individuals, institutes and countries, and trends by the frequency of keywords used. Over the years, there had been a notably growth trend in research outputs, along with more participation and collaboration of institutes and countries. Research collaborative papers shifted from national inter-institutional to international collaboration. The decreasing share of world total and independent articles by the seven major industrialized countries (G7) was examined. Aerosol research in environmental and chemical related fields other than in medical fields was the mainstream of current years. Finally, author keywords, words in title and keywords plus were analyzed contrastively, with research trends and recent hotspots provided.

http://dns2.asia.edu.tw/~ysho/YSHO-English/Publications/PDF/Scientometrics77,%20113.pdf

Assessment of world aerosol research trends by bibliometric analysis

Bibliometric analysis of global environmental assessment research in a 20-year period

As the population ages in Taiwan, stroke research has received greater attention in recent years. Strokes have significant impacts on the health and well-being of the elderly. To formulate future research policy, information on stroke publications should be collected. In this research, we studied stroke-related research articles published by Taiwan researchers which were indexed in the Science Citation Index from 1991 to 2005. We found that the quantity of publications has increased at a quicker pace than the worldwide trend. Over the years, there has been an increase in international collaboration, mainly with researchers in the U.S. Article visibility, measured as the frequency of being cited, also increased during the period. It appears that stroke research in Taiwan has become more globally connected and has also improved in quality. The publication output was concentrated in a few institutes, but there was a wide variation among these institutes in the ability to independently conduct research. A wide array of keywords indicated a probable lack of continuity in research. Nevertheless, there was an inverse relationship between stroke mortality and number of published articles in Taiwan. To improve the quality and efficiency of stroke research, continuity in research focuses needs to be maintained, and thus funding should be allocated on a long-term basis to institutes with a proven record of success.

http://dns2.asia.edu.tw/~ysho/YSHO-English/Publications/PDF/Scientometrics72,%20201.pdf

A bibliometric and citation analysis of stroke-related research in Taiwan

Background The recognition of anatomical variants is essential in preoperative planning for lung cancer surgery. Although three-dimensional (3-D) reconstruction provided an intuitive demonstration of the anatomical structure, the recognition process remains fully manual. To render a semiautomated approach for surgery planning, we developed an artificial intelligence (AI)–based chest CT semantic segmentation algorithm that recognizes pulmonary vessels on lobular or segmental levels. Hereby, we present a retrospective validation of the algorithm comparing surgeons’ performance. Methods The semantic segmentation algorithm to be validated was trained on non-contrast CT scans from a single center. A retrospective pilot study was performed. An independent validation dataset was constituted by an arbitrary selection from patients who underwent lobectomy or segmentectomy in three institutions during Apr. 2020 to Jun. 2021. The golden standard of anatomical variants of each enrolled case was obtained via expert surgeons’ judgments based on chest CT, 3-D reconstruction, and surgical observation. The performance of the algorithm is compared against the performance of two junior thoracic surgery attendings based on chest CT. Results A total of 27 cases were included in this study. The overall case-wise accuracy of the AI model was 82.8% in pulmonary vessels compared to 78.8% and 77.0% for the two surgeons, respectively. Segmental artery accuracy was 79.7%, 73.6%, and 72.7%; lobular vein accuracy was 96.3%, 96.3%, and 92.6% by the AI model and two surgeons, respectively. No statistical significance was found. In subgroup analysis, the anatomic structure-wise analysis of the AI algorithm showed a significant difference in accuracies between different lobes (p = 0.012). Higher AI accuracy in the right-upper lobe (RUL) and left-lower lobe (LLL) arteries was shown. A trend of better performance in non-contrast CT was also detected. Most recognition errors by the algorithm were the misclassification of LA1+2 and LA3. Radiological parameters did not exhibit a significant impact on the performance of both AI and surgeons. Conclusion The semantic segmentation algorithm achieves the recognition of the segmental pulmonary artery and the lobular pulmonary vein. The performance of the model approximates that of junior thoracic surgery attendings. Our work provides a novel semiautomated surgery planning approach that is potentially beneficial to lung cancer patients.

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AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

Ya-Li Huang

Papers

A bibliometric and citation analysis of stroke-related research in Taiwan

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels