Artificial intelligence and perinatology: a study on accelerated academic production- a bibliometric analysis.

IF 3.1 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Frontiers in Medicine Pub Date : 2025-02-19 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1505450
Ümran Kılınçdemir Turgut
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Abstract

Objective: The main purpose of this bibliometric study is to compile the rapidly increasing articles in the field of perinatology in recent years and to shed light on the research areas where studies are concentrated.

Materials and methods: This bibliometric study was conducted using the Thomson ISI Web of Science Core Collection (WOSCC) system on May 4, 2024, with specific keywords. The abstracts of 1,124 articles that met the criteria were reviewed, and 382 articles related to perinatology were evaluated. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19). Out of these, 121 articles with 10 or more citations were analyzed in terms of their content and categorized under the headings "Purpose of Evaluation," "Medical Methods and Parameters Used," "Output To Be Evaluated," and "Fetal System or Region Being Evaluated."

Results: In this bibliometric study, it was found that the most frequently published journal among the 382 examined articles was Medical Image Analysis, while the journals with the most publications in the field of perinatology were Prenatal Diagnosis and Ultrasound in Obstetrıcs & Gynecology. The most commonly used keyword was "deep learning" (115/382). Among the 121 highly cited articles, the most common purpose of evaluation was "Prenatal Screening." Artificial intelligence was most frequently used in ultrasound (59.8%) imaging, with MRI (20.5%) in second place. Among the evaluated outputs, "organ scanning" (35/121) was in first place, while "biometry" (34/121) was in second place. In terms of evaluated systems and organs, "growth screening" (35/121) was the most common, followed by the "neurological system" (33/121) and then the "cardiovascular system" (18/121).

Conclusion: I has witnessed the increasing influence of artificial intelligence in the field of perinatology in recent years. This impact may mark the historic beginning of the transition to the AI era in perinatology. Milestones are being laid on the path from prenatal screening to prenatal treatment.

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人工智能与围产学:加速学术产出的研究——文献计量学分析。
目的:本文献计量学研究的主要目的是汇编近年来在围产期领域快速增长的文章,揭示研究的重点领域。材料与方法:本文献计量学研究于2024年5月4日使用Thomson ISI Web of Science Core Collection (wscc)系统进行,并带有特定的关键词。对符合标准的1124篇文献的摘要进行了综述,对382篇与围产期相关的文献进行了评价。使用VOSviewer (version 1.6.19)软件进行关键词共现、作者共被引和参考文献共被引分析。其中,引用次数在10次或以上的121篇文章根据内容进行了分析,并按“评估目的”、“使用的医疗方法和参数”、“待评估的输出”和“待评估的胎儿系统或区域”等标题进行了分类。结果:本文献计量学研究发现,在382篇被检查的文章中,发表频率最高的期刊是医学图像分析,而在围产期领域发表次数最多的期刊是Obstetrıcs & Gynecology中的产前诊断和超声。最常使用的关键词是“深度学习”(115/382)。在121篇高引用文章中,最常见的评价目的是“产前筛查”。人工智能最常用于超声成像(59.8%),其次是核磁共振成像(20.5%)。在评估的输出中,“器官扫描”(35/121)排名第一,“生物测量”(34/121)排名第二。在评估的系统和器官方面,“生长筛选”(35/121)最为常见,其次是“神经系统”(33/121),然后是“心血管系统”(18/121)。结论:近年来,人工智能在围产医学领域的影响越来越大。这一影响可能标志着围产期向人工智能时代过渡的历史性开端。在从产前筛查到产前治疗的道路上正在树立里程碑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Medicine
Frontiers in Medicine Medicine-General Medicine
CiteScore
5.10
自引率
5.10%
发文量
3710
审稿时长
12 weeks
期刊介绍: Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world
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