Meram School of Medicine, Urology Department, Necmettin Erbakan University, Konya, Türkiye.
School of Medicine, Urology Department, Trakya University, Edirne, Türkiye.
World J Urol. 2024 Oct 17;42(1):579. doi: 10.1007/s00345-024-05268-8.
Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness.
The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed.
Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65-1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods.
The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions-diagnosis, monitoring, and treatment-AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care.
与全球趋势一致，人工智能在医学领域取得了进展，尤其是在尿石症方面。它有望实现准确诊断、有效治疗，并预测流行病学风险和结石排出。本系统评价旨在确定用于尿石症研究的 AI 模型类型，并评估其效果。
本研究已在 PROSPERO 注册。使用“泌尿外科”、“人工智能”和“机器学习”等关键词，在 Pubmed、EMBASE、Google Scholar 和 Cochrane Library 数据库中搜索相关文献。仅纳入关于尿石症的原始 AI 研究，排除综述、不相关的研究和非英语文章。遵循 PRISMA 指南。
在最初确定的 4851 项研究中，有 71 项被纳入 AI 在尿石症中的应用的全面分析。AI 在 12 项结石成分分析研究中表现出显著的能力，平均精度为 88.2%（范围为 0.65-1）。在结石检测领域，平均精度达到了 96.9%。AI 预测自发性输尿管结石排出的准确率平均为 87%，而其在 PCNL 和 SWL 等治疗方式中的性能则分别达到了 82%和 83%的平均准确率。这些 AI 模型总体上优于传统的诊断和治疗方法。
综合数据强调了 AI 在尿石症管理中的日益重要性。在诊断、监测和治疗等各个方面，AI 均优于传统方法。高精度和准确率表明 AI 不仅有效，而且有望整合到常规临床实践中。需要进一步研究以确定 AI 的长期效用，并验证其作为泌尿外科护理标准工具的作用。
