ArticleEuropean urology open science2025
Harnessing Advanced Imaging Technologies in Robotic Urological Surgery Requires Specialized and Structured Training.
Article in European urology open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Precise and minimally invasive localization of pelvic nodes during robot-assisted surgery using image-guided navigation.International journal of computer assisted radiology and surgery · 2026Article
- The times they are (still) a-Changin'.Prostate cancer and prostatic diseases · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the integration of advanced technologies in surgery has transformed health care, with urology consistently pioneering these innovations. Unlike the structured drug development process, surgical advancements follow a distinct pathway, exemplified by the Innovation, Development, Exploration, Assessment, and Long-term follow-up (IDEAL) framework, which supports safe, evidence-based innovation. Complementary technologies, including three-dimensional (3D) models and augmented reality (AR), have emerged as transformative tools in robotic urological surgery, particularly for complex cases. These technologies enhance surgical planning, intraoperative navigation, and postoperative outcomes, especially in kidney and prostate cancer surgeries. Despite growing adoption, structured training programs tailored to 3D and AR applications remain underdeveloped. To address this gap, we conducted an online survey of participants from the 12th Techno Urology Meeting (2024) to evaluate the current use and training needs for these technologies. Among 126 respondents, the majority utilized 3D models (64.5%) and AR (40.3%) in clinical practice, highlighting their role in robotic partial nephrectomy and radical prostatectomy. Respondents overwhelmingly emphasized the need for dedicated multidisciplinary courses with hands-on training and proficiency-based progression methodologies. This study underscores the necessity of structured, technology-specific training to maximize the potential of 3D models and AR in robotic urology, offering critical insights for advancing surgical education and improving patient care outcomes. Patient summary: This study highlights the increasing integration of advanced technologies such as three-dimensional models and augmented reality in urological surgery, and emphasizes the growing demand for proper training to maximize their potential. While demonstration of their direct impact on surgical precision and outcomes would require a different study design, this analysis primarily underscores the need to establish comprehensive training programs to ensure safer and more effective care for patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.