目的:分析“人工智能(AI)及其子集(机器学习-ML,虚拟现实-VR,在过去的30年里,影像组学)在尿石症中。我们研究了与人工智能和结石疾病相关的出版物趋势,包括临床和外科应用,和腔内泌尿外科的培训。
方法:尽管对PubMed进行了细目研究,我们从1994-2023年对所有发表的关于“人工智能”的论文进行了全面审查,ML,VR和Radiomics\"。然后将论文分为三类:A-临床(非手术),B-临床(外科)和C-培训论文,然后将文章分配给3个时期:1期(1994-2003年),Period-2(2004-2013),第3期(2014-2023年)。
结果:发现343篇论文(A-129、B-163和C-51组),趋势从第1期增加到第2期,为123%(p=0.009),到第3期,为453%(p=0.003)。A组从第2期增加到第3期,B和C为476%(p=0.019),分别为616%(0.001)和185%(p<0.001)。A组论文包括关于“石头特征”的论文中的上升(+2100%;p=0.011),“肾功能”(p=0.002),“结石诊断”(+192%),“石头通道预测”(+400%)和“生活质量”(+1000%)。B组论文包括“URS”论文中的上升(+2650%,p=0.008),“PCNL”(+600%,p=0.001)和“SWL”(+650%,p=0.018)。关于“目标”的论文(+453%,p<0.001),“结果”(+850%,p=0.013)和“技术创新”(p=0.0311)呈上升趋势。C组论文包括“PCNL”论文中的上升(+300%,p=0.039),和“URS”(+188%,p=0.003)。
结论:在过去十年中,有关AI及其尿石症子集领域的出版物呈指数增长,随着手术和非手术临床领域以及培训的增加。未来在腔内泌尿外科和尿石症领域与AI相关的增长可能会改善培训,以患者为中心的决策和临床结果。
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 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: Through a MeshTerms research on PubMed, we performed a comprehensive review from 1994-2023 for all published articles on \"AI, ML, VR, and Radiomics.\" Articles were then divided into three categories as follows: A-Clinical (Nonsurgical), B-Clinical (Surgical), and C-Training articles, and articles were then assigned to following three periods: Period-1 (1994-2003), Period-2 (2004-2013), and Period-3 (2014-2023). Results: A total of 343 articles 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 articles included rise in articles 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 articles included rise in articles on \"URS\" (+2650%, p = 0.008), \"PCNL\"(+600%, p = 0.001), and \"SWL\" (+650%, p = 0.018). Articles on \"Targeting\" (+453%, p < 0.001), \"Outcomes\" (+850%, p = 0.013), and \"Technological Innovation\" (p = 0.0311) had rising trends. Group C articles included rise in articles 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 nonsurgical 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.