Presented by: Carlotta Nedbal MD
Urology Unit, Azienda Ospedaliero-Universitaria delle Marche, Università Politecnica delle Marche, Ancona, Italy, AND Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.

Introduction

To analyze the bibliometric publication trend on the application of “Artificial Intelligence (AI) and its subsets (Machine Learning – ML, Virtual reality – VR, radiomics etc.) in Urolithiasis” in a period of 30 years from 1994 to 2023. We conducted this study looking at the publication trend associated with AI and stone disease, including both clinical and surgical applications and training in endourology.

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 and Radiomics” in “Urolithiasis” with papers in all languages included in the final review. Papers were then divided in three major categories: A - Clinical (Non-surgical), B -Clinical (surgical) and C - Training papers. According to year of publication, each article was then assigned to one of three time periods (decades): Period1 (1994-2003), Period2 (2004-2013), Period3 (2014-2023).

Results

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Over a 30-year time, 343 papers have been published on the subject, including 319 in English language and 24 in non-English language. Groups A, B and C included 129, 163 and 51 papers respectively and there was an overall increase from Period1 to Period2 of 123%(p=0.009) and to period 3 of 453%(p=0.003). This increase from Period2 to Period3 for groups A, B and C was 476%(p=0.019), 616%(0.001) and 185%(p<0.001) respectively.

Group A papers included rise from Period2 to Period3 in papers on “stone characteristics” (+2100%;p=0.011) and in “renal function” (+6000%, p=0.002), “stone diagnosis” (+192%, p=0.123), “prediction of stone passage” (+400%, p=0.232) and “quality of life” (+1000%, p=0.331).Group B papers included rise in papers in “URS”(+2650%, p=0.008), “PCNL” (+600%, p=0.001) and “SWL” (+650%,p=0.018). A second analysis on surgical papers pointed out that papers on “Stone targeting” (+453%, p<0.001), “Outcomes” (+850%,p=0.013) and “Technological Innovation” (p=0.0311) had rising trends. Group C papers included rise in papers in “PCNL” (+300%, p=0.039), followed by a positive trend of “URS” (+188%,p=0.003).

Conclusion

Publications on AI and its subset areas for urolithiasis have seen an exponential increase over the last 3 decades and in particular over the last decade, with an increase in surgical and non-surgical clinical areas as well as in training. While applications related to new technology has fuelled this, PCNL particularly seems to garner most interest. Future AI related growth in the field of endourology and urolithiasis is likely to improve training and patient centered decision making and clinical outcomes.

Funding

None

Lead Authors

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

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

Co-Authors

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

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

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

Bhaskar Kumar Somani, MD, Professor
Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.