Guest Editor(s)
Prof. Bhaskar Somani
Email: bhaskarsomani@yahoo.com
Affiliation: University Hospital Southampton NHS Foundation Trust, Southampton, null, United Kingdom
Homepage:
Research Interests: endourology (stones, BPH)
Prof. Olivier Traxer
Email: traxer.olivier@gmail.com
Affiliation: Sorbonne Université, Service d'Urologie, AP-HP, Hôpital Tenon, Paris, France
Homepage:
Research Interests: endourology, kidney stone management
Summary
Endourology has witnessed a remarkable technological progress over the past two decades, transforming the management of kidney stone disease (KSD), benign prostatic obstruction (BPO), and other urological conditions. Advances in flexible endoscopy, laser technologies, suction-assisted procedures, image-guided interventions, robotics, and minimally invasive surgical techniques have significantly improved procedural efficacy, safety, and patient outcomes. More recently, artificial intelligence (AI), machine learning, and predictive analytics have emerged as promising tools to support clinical decision-making, personalize treatment strategies, and optimize perioperative care.
Despite these advances, important challenges remain regarding procedural safety, intrarenal pressure control, thermal effects during laser lithotripsy, optimal surgical training, integration of robotic technologies, and implementation of AI-driven solutions into routine clinical practice. As innovation continues to accelerate, there is a growing need to critically evaluate emerging technologies and generate high-quality evidence to guide their adoption.
This special issue seeks to provide a comprehensive platform for showcasing contemporary developments and future directions in endourology and minimally invasive urological surgery, bringing together clinical, translational, technological, and educational research from international experts.
Aim and Scope of the Special Issue
The aim of this Special Issue is to highlight innovative research, emerging technologies, and evidence-based advances that are shaping the future of endourology and minimally invasive urological surgery.
We welcome original research articles, systematic reviews, meta-analyses, multicentre collaborative studies, experimental investigations, technological evaluations, clinical outcome studies, consensus statements, and expert reviews. Submissions should focus on improving surgical outcomes, enhancing patient safety, advancing procedural technologies, and facilitating precision medicine approaches within urology.
Particular emphasis will be placed on translational research and technologies that bridge innovation with clinical implementation.
Potential topics include, but are not limited to:
· Intrarenal pressure monitoring, irrigation dynamics, and suction technologies
· Flexible aspiration-enabled systems (FANS) and suction-assisted ureteroscopy
· Thermal safety during laser lithotripsy and endourological procedures
· Advances in laser technologies and stone fragmentation strategies
· Flexible ureteroscopy and retrograde intrarenal surgery (RIRS)
· Percutaneous nephrolithotomy (PCNL) and novel access techniques
· Image-guided surgery, fusion imaging, and navigation systems
· Artificial intelligence and machine learning applications in Endourology
· Predictive analytics for surgical outcomes and clinical decision-making
· Personalized postoperative management and stenting strategies
· Robotics in endourology and minimally invasive urological surgery
· Evolution of laparoscopic and robotic surgical training
· Anatomical endoscopic enucleation of the prostate (AEEP) and BPH surgery
· Surgical management of elderly and frail urological patients
· Novel devices, digital technologies, and smart operating room solutions
· Patient safety, quality improvement, and outcome optimization
· Multicentre collaborative research and real-world evidence studies
· Future directions and emerging innovations in endourology
Keywords
endourology, urolithiasis, flexible ureteroscopy, percutaneous nephrolithotomy, intrarenal pressure, laser lithotripsy, artificial intelligence, machine learning, robotic surgery, minimally invasive urology.
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