Purpose: To analyze the bibliometric publication trend on the application of "Artificial Intelligence (AI) and its subsets (Machine Learning-ML, Virtual reality-VR, Radiomics) in Urolithiasis" over the last 3 decades. We looked at the publication trends associated with AI and stone disease, including both clinical and surgical applications, and training in endourology.
Methods: Though a MeshTerms research on PubMed, we performed a comprehensive review from 1994-2023 for all published papers on "AI, ML, VR and Radiomics". Papers were then divided in three categories: A-Clinical (Non-surgical), B-Clinical (Surgical) and C-Training papers, and articles were then assigned to 3 periods: Period-1 (1994-2003), Period-2 (2004-2013), Period-3 (2014-2023).
Results: 343 papers were noted (Groups A-129, B-163 and C-51), and trends increased from Period-1 to Period-2 at 123% (p=0.009), and to period-3 at 453% (p=0.003). This increase from Period-2 to Period-3 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 in papers on "stone characteristics" (+2100%;p=0.011), "renal function" (p=0.002), "stone diagnosis" (+192%), "prediction of stone passage" (+400%) and "quality of life" (+1000%). Group B papers included rise in papers on "URS" (+2650%,p=0.008), "PCNL" (+600%, p=0.001) and "SWL" (+650%,p=0.018). Papers on "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 on "PCNL" (+300%,p=0.039), and "URS" (+188%,p=0.003).
Conclusion: Publications on AI and its subset areas for urolithiasis have seen an exponential increase over the last decade, with an increase in surgical and non-surgical clinical areas as well as in training. Future AI related growth in the field of endourology and urolithiasis is likely to improve training, patient centered decision making and clinical outcomes.