Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis
Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.990708/full |
| _version_ | 1811261861832687616 |
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| author | Demeng Xia Gaoqi Chen Kaiwen Wu Mengxin Yu Zhentao Zhang Yixian Lu Lisha Xu Yin Wang |
| author_facet | Demeng Xia Gaoqi Chen Kaiwen Wu Mengxin Yu Zhentao Zhang Yixian Lu Lisha Xu Yin Wang |
| author_sort | Demeng Xia |
| collection | DOAJ |
| description | Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultrasonic field from 2011 to 2021 was screened by authors from the Web of Science Core Collection, which aims to summarize the trend of artificial intelligence application in the field of ultrasound, meanwhile, visualize and predict research hotspots. A total of 908 publications were included in the study. Overall, the number of global publications is on the rise, and studies on the application of artificial intelligence in the field of ultrasound continue to increase. China has made the largest contribution in this field. In terms of institutions, Fudan University has the most number of publications. Recently, IEEE Access is the most published journal. Suri J. S. published most of the articles and had the highest number of citations in this field (29 articles). It's worth noting that, convolutional neural networks (CNN), as a kind of deep learning algorithm, was considered to bring better image analysis and processing ability in recent most-cited articles. According to the analysis of keywords, the latest keyword is “COVID-19” (2020.8). The co-occurrence analysis of keywords by VOSviewer visually presented four clusters which consisted of “deep learning,” “machine learning,” “application in the field of visceral organs,” and “application in the field of cardiovascular”. The latest hot words of these clusters were “COVID-19; neural-network; hepatocellular carcinoma; atherosclerotic plaques”. This study reveals the importance of multi-institutional and multi-field collaboration in promoting research progress. |
| first_indexed | 2024-04-12T19:13:29Z |
| format | Article |
| id | doaj.art-509d00155a244018910dcb977b6bfa5d |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-12T19:13:29Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-509d00155a244018910dcb977b6bfa5d2022-12-22T03:19:48ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-09-011010.3389/fpubh.2022.990708990708Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysisDemeng Xia0Gaoqi Chen1Kaiwen Wu2Mengxin Yu3Zhentao Zhang4Yixian Lu5Lisha Xu6Yin Wang7Luodian Clinical Drug Research Center, Shanghai Baoshan Luodian Hospital, Shanghai University, Shanghai, ChinaDepartment of Pancreatic Hepatobiliary Surgery, Changhai Hospital, Naval Medical University, Shanghai, ChinaDepartment of Gastroenterology, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, Chengdu, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, ChinaDepartment of Clinical Medicine, The Naval Medical University, Shanghai, ChinaDepartment of Clinical Medicine, The Naval Medical University, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, ChinaDepartment of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, ChinaUltrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultrasonic field from 2011 to 2021 was screened by authors from the Web of Science Core Collection, which aims to summarize the trend of artificial intelligence application in the field of ultrasound, meanwhile, visualize and predict research hotspots. A total of 908 publications were included in the study. Overall, the number of global publications is on the rise, and studies on the application of artificial intelligence in the field of ultrasound continue to increase. China has made the largest contribution in this field. In terms of institutions, Fudan University has the most number of publications. Recently, IEEE Access is the most published journal. Suri J. S. published most of the articles and had the highest number of citations in this field (29 articles). It's worth noting that, convolutional neural networks (CNN), as a kind of deep learning algorithm, was considered to bring better image analysis and processing ability in recent most-cited articles. According to the analysis of keywords, the latest keyword is “COVID-19” (2020.8). The co-occurrence analysis of keywords by VOSviewer visually presented four clusters which consisted of “deep learning,” “machine learning,” “application in the field of visceral organs,” and “application in the field of cardiovascular”. The latest hot words of these clusters were “COVID-19; neural-network; hepatocellular carcinoma; atherosclerotic plaques”. This study reveals the importance of multi-institutional and multi-field collaboration in promoting research progress.https://www.frontiersin.org/articles/10.3389/fpubh.2022.990708/fullbibliometricsartificial intelligenceultrasoundCNNCOVID-19 |
| spellingShingle | Demeng Xia Gaoqi Chen Kaiwen Wu Mengxin Yu Zhentao Zhang Yixian Lu Lisha Xu Yin Wang Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis Frontiers in Public Health bibliometrics artificial intelligence ultrasound CNN COVID-19 |
| title | Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis |
| title_full | Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis |
| title_fullStr | Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis |
| title_full_unstemmed | Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis |
| title_short | Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis |
| title_sort | research progress and hotspot of the artificial intelligence application in the ultrasound during 2011 2021 a bibliometric analysis |
| topic | bibliometrics artificial intelligence ultrasound CNN COVID-19 |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.990708/full |
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