UT Southwestern Medical Center.
Professor of Urology and Internal Medicine, Charles and Jane Pak Center for Mineral Metabolism, UT Southwestern Medical Center, Dallas, TX, USA.
Curr Opin Urol. 2021 Jul 1;31(4):391-396. doi: 10.1097/MOU.0000000000000896.
Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients.
AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms.
The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.
目的综述：人工智能（AI）是机器或计算机模拟智能行为的能力。在医学领域，利用大型数据集使计算机能够学习如何执行认知任务，从而促进医学决策。本综述旨在描述 AI 在结石病中的进展，以提高确定结石成分的诊断准确性，预测手术或观察等待的结果，并最终优化患者的治疗选择。
最新发现：AI 算法在结石检测和手术结果预测等不同领域显示出很高的准确性。有用于经皮肾镜碎石术、体外冲击波碎石术和输尿管结石排出的机器学习算法。其中一些算法与现有的评分系统和列线图相比，具有更好的预测能力。
总结：AI 的使用可以促进结石病患者诊断和治疗算法的发展。尽管这些算法的泛化能力和外部有效性仍不确定，但开发高度准确的基于 AI 的工具可能使泌尿科医生能够提供更个性化的患者护理和更好的结果。
