Presented by: carlottaC Nedbal MD
Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy., AND Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom.

Introduction

To analyze the trend of publications in a period of 30 years from 1994 to 2023, on the application of “Artificial Intelligence (AI), Machine Learning (ML), Virtual reality (VR) and Radiomics in PCNL”. We conducted this study looking at published papers associated with AI and PCNL procedures, including simulation training, with preoperative and intraoperative applications.

Materials

Though a MeshTerms research on PubMed database, we performed a comprehensive review of the literature from 1994 to 2023 for all published papers on “AI, ML, VR, Radiomics” in “PCNL”, with papers in all languages included in the final review. Papers were divided into three 10-years periods according to year of publication: Period-1 (1994-2003), Period-2 (2004-2013), Period-3 (2014-2023).

Results

,

Over a 30-year timeframe, 143 papers have been published on the subject with 116 (81%) published in the last decade, with a relative increase from Period-2 to Period-3 of +427% (p=0.0027). There was a gradual increase of areas such as automated diagnosis of larger stones, automated intraoperative needle targeting and VR simulators in surgical planning and training. This increase was most marked in Period-3 with automated targeting with 52 papers (45%), followed by application of AI, ML and radiomics in predicting operative outcomes (22%, n=26) and VR for simulation (18%, n=21). Papers on technological innovations in PCNL (n=9), intelligent construction of personalized protocols (n=6) and automated diagnosis (n=2) accounted for 15% of publications. Comparing Period-2 and Period-3, the rise in publication on automated targeting for PCNL was significant at + 247% (p=0.0055). Similarly, an increasing trend by +200% was seen in publication on PCNL training (p=0.0161).

Conclusion

An interest in the application of AI in PCNL procedures has increased in the last 30 years, and a steep rise has been witnessed in the last 10 years. As new technologies are developed, their application in devices for training and automated systems for precise renal puncture and outcome prediction seems to play the leading role in modern day AI based publication trends on PCNL.

Funding

None

Lead Authors

Victoria Jahrreiss, MD
Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom AND Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.

Clara Cerrato, MD
Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom

Co-Authors

Daniele Castellani, MD
Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy.

Amelia Pietropaolo, MD
Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom.

Andrea Benedetto Galosi, Professor
Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Polytechnic University of Marche, Ancona, Italy.

Bhaskar Kumar Somani, Professor
Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, United Kingdom.