The role of ‘artificial intelligence, machine learning, virtual reality, and radiomics’ in PCNL: a review of publication trends over the last 30 years

• Published in: Therapeutic Advances in Urology Vol. 15; p. 17562872231196676
• Main Authors: Nedbal, Carlotta, Cerrato, Clara, Jahrreiss, Victoria, Castellani, Daniele, Pietropaolo, Amelia, Galosi, Andrea Benedetto, Somani, Bhaskar Kumar
• Format: Book Review Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2023; Sage Publications Ltd; SAGE Publishing
• Subjects: Artificial intelligence; Automation; Machine learning; Radiomics; Trends; Virtual reality; Virtual, Augmented and Mixed Reality in Urology-From Prevention to Follow-up; radiomics; PCNL; simulation; kidney calculi; urolithiasis; machine learning; Artificial intelligence
• ISSN: 1756-2872; 1756-2880
• DOI: 10.1177/17562872231196676

Introduction:
We wanted 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 percutaneous nephrolithotomy (PCNL)’. We conducted this study by looking at published papers associated with AI and PCNL procedures, including simulation training, with preoperative and intraoperative applications.
Materials and Methods:
Although MeSH terms research on the PubMed database, we performed a comprehensive review of the literature from 1994 to 2023 for all published papers on ‘AI, ML, VR, and radiomics’ in ‘PCNL’, with papers in all languages included. Papers were divided into three 10-year periods: Period 1 (1994–2003), Period 2 (2004–2013), and 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 in 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 the 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. A rise in automated targeting for PCNL and PCNL training between Period 2 and Period 3 was +247% (p = 0.0055) and +200% (p = 0.0161), respectively.
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 a leading role in modern-day AI-based publication trends on PCNL.