STORZ MEDICAL – Literature Databases
STORZ MEDICAL – Literature Databases
Literature Databases
Literature Databases

Nedbal C. et al., 2023: Trends of 'Artificial Intelligence, Machine Learning, Virtual Reality and Radiomics in Urolithiasis' over the last 30 years (1994-2023) as published in the literature (PubMed): a Comprehensive review.

Nedbal C, Cerrato C, Jahrreiss V, Pietropaolo A, Galosi AB, Castellani D, Somani BK.
University Hospital Southampton NHS Foundation Trust, 7425, Urology, Southampton, United Kingdom of Great Britain and Northern Ireland;
University Hospital Southampton NHS Foundation Trust, 7425, Urology, Southampton, United Kingdom of Great Britain and Northern Ireland;
University Hospital Southampton NHS Foundation Trum.st, 7425, Urology, Southampton, United Kingdom of Great Britain and Northern Ireland;
University Hospital Southampton NHS Foundation Trust, 7425, Urology, Southampton, United Kingdom of Great Britain and Northern Ireland;
Azienda Ospedaliero Universitaria Ospedali Riuniti di Ancona Umberto I G M Lancisi G Salesi, 18494, Urology, Via Conca, Ancona, Marche, Italy, I-60100.
Polytechnic University of Marche, 9294, Ancona, Italy, 60121;
AOU Ospedali Riuniti di Ancona, 18494, via conca 71, Ancona, Italy, 60126;
University Hospital Southampton NHS Foundation Trust, 7425, Urology, Southampton, United Kingdom of Great Britain and Northern Ireland

Abstract

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.

J Endourol. 2023 Oct 26. doi: 10.1089/end.2023.0263. Online ahead of print.PMID: 37885228

0
 

Comments 1

Hans-Göran Tiselius on Friday, 03 May 2024 11:00

This article summarizes the trends of:

AI artificial intelligence
ML machine learning
VR virtual reality
Radiomics

used in patients with urolithiasis.

Although AI occasionally has been used for prediction of SWL outcome, the report has no specific reference to SWL. In this regard it should be noted that whereas radiologic and other pre-treatment features reasonably well can be standardized, the weak point in the chain is that SWL can be carried out in so many different ways and with different care by operators.

Nevertheless, the exponential application of the modern methods mentioned in the article shows that this kind of measurements and analyses will become increasingly common in the future, both for diagnostic purposes and for prediction of treatment outcomes.

Hans-Göran Tiselius

This article summarizes the trends of: AI artificial intelligence ML machine learning VR virtual reality Radiomics used in patients with urolithiasis. Although AI occasionally has been used for prediction of SWL outcome, the report has no specific reference to SWL. In this regard it should be noted that whereas radiologic and other pre-treatment features reasonably well can be standardized, the weak point in the chain is that SWL can be carried out in so many different ways and with different care by operators. Nevertheless, the exponential application of the modern methods mentioned in the article shows that this kind of measurements and analyses will become increasingly common in the future, both for diagnostic purposes and for prediction of treatment outcomes. Hans-Göran Tiselius
Saturday, 13 July 2024