Van Huele Andries, Demeulemeester Jelle, Everaert Karel, Petrovic Mirko, Calders Patrick, Hervé François, Wagg Adrian, Bou Kheir George
Department of Urology, AZ Alma, Eeklo, Belgium.
Department of Urology, Ghent University Hospital, Ghent, Belgium.
World J Urol. 2025 Jul 16;43(1):438. doi: 10.1007/s00345-025-05805-z.
This artificial intelligence (AI)-driven scientometric analysis, conducted using the Mynd discovery platform, explores research trends in lower urinary tract symptoms (LUTS) among older patients. By applying its novel recency metric, the study identified emerging areas, longstanding research themes, and critical gaps in literature.
Mynd applies AI-driven scientometric analysis to map research trends in LUTS and frailty using PubMed abstracts. A total of 13,737 PubMed-indexed publications were analyzed. Through unsupervised topic modeling, Mynd extracts key terminology and builds hierarchical topic structures to enhance contextual understanding. Quantitative metrics-such as the novel recency metric-measure publication trends, categorizing topics as emerging, mainstream, declining, or hot. This approach enables data-driven insights into LUTS research in older persons.
While research on LUTS has grown steadily since the 1980s, a decline in publication output has been observed since 2020. Geographical analysis reflects a shift in scientific prominence towards Asia. More in-depth analysis reveals a shift towards minimally invasive diagnostic methods, with a decline in research interest in invasive urodynamics. A similar pattern is observed in therapeutics. Frailty remains significantly underrepresented in literature, accounting for only 2.5% of the related studies, yet its high recency score indicates a rising focus.
These insights underscore the evolving landscape of LUTS research, with growing attention to patient-centered, less invasive management strategies. However, major research gaps persist, particularly in the study of frail patients, necessitating further investigations to ensure evidence-based approaches tailored to aging populations.
这项使用Mynd发现平台进行的人工智能驱动的科学计量分析，探索了老年患者下尿路症状（LUTS）的研究趋势。通过应用其新颖的时效性指标，该研究确定了新兴领域、长期存在的研究主题以及文献中的关键空白。
Mynd应用人工智能驱动的科学计量分析，利用PubMed摘要来描绘LUTS和虚弱症的研究趋势。总共分析了13737篇被PubMed索引的出版物。通过无监督主题建模，Mynd提取关键术语并构建层次化主题结构，以增强背景理解。诸如新颖的时效性指标等定量指标衡量出版物趋势，将主题分为新兴、主流、衰退或热门。这种方法能够对老年人LUTS研究进行数据驱动的洞察。
虽然自20世纪80年代以来，关于LUTS的研究稳步增长，但自2020年以来已观察到出版物产出下降。地理分析反映出科学影响力向亚洲转移。更深入的分析显示向微创诊断方法转变，对侵入性尿动力学的研究兴趣下降。在治疗方面也观察到类似模式。虚弱症在文献中的代表性仍然显著不足，仅占相关研究的2.5%，但其高时效性得分表明关注度在上升。
这些见解强调了LUTS研究的不断演变态势，对以患者为中心、侵入性较小的管理策略的关注度不断提高。然而，主要研究空白仍然存在，特别是在虚弱患者的研究方面，需要进一步调查以确保为老年人群量身定制基于证据的方法。
