School of Rehabilitation, Capital Medical University, Beijing, China.
Department of Urology, China Rehabilitation Research Centre, Beijing, China.
Neurourol Urodyn. 2024 Sep;43(7):1617-1625. doi: 10.1002/nau.25490. Epub 2024 Jun 4.
Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are increasingly applied to the field of urodynamics. However, no studies have evaluated how to select appropriate algorithm models for different urodynamic research tasks.
We undertook a narrative review evaluating how the published literature reports the applications of machine learning in urodynamics. We searched PubMed up to December 2023, limited to the English language. We selected the following search terms: artificial intelligence, machine learning, deep learning, urodynamics, and lower urinary tract symptoms. We identified three domains for assessment in advance of commencing the review. These were the applications of urodynamic studies examination, applications of diagnoses of dysfunction related to urodynamics, and applications of prognosis prediction.
The machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, which are urodynamic examination, diagnosis of urinary tract dysfunction and prediction of the efficacy of various treatment methods. Most of these studies were single-center retrospective studies, lacking external validation, requiring further validation of model generalization ability, and insufficient sample size. The relevant research in this field is still in the preliminary exploration stage; there are few high-quality multi-center clinical studies, and the performance of various models still needs to be further optimized, and there is still a distance from clinical application.
At present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics. The purpose of this review is to summarize and classify the machine learning algorithms applied in this field and to guide researchers to select the appropriate algorithm model for different task requirements to achieve the best results.
机器学习算法作为一种研究工具,包括传统机器学习和深度学习,越来越多地应用于尿动力学领域。然而,目前尚无研究评估如何为不同的尿动力学研究任务选择合适的算法模型。
我们进行了一项叙述性综述,评估了机器学习在尿动力学中的应用文献报告方式。我们检索了截至 2023 年 12 月的 PubMed 数据库,仅限于英文文献。我们选择了以下搜索词:人工智能、机器学习、深度学习、尿动力学和下尿路症状。在开始综述之前,我们预先确定了三个评估领域。这三个领域分别是尿动力学研究检查的应用、与尿动力学相关的功能障碍诊断的应用以及预后预测的应用。
尿动力学领域应用的机器学习算法主要可以分为三个方面,分别是尿动力学检查、尿路功能障碍诊断和各种治疗方法疗效预测。这些研究大多为单中心回顾性研究,缺乏外部验证,需要进一步验证模型的泛化能力,且样本量不足。该领域的相关研究仍处于初步探索阶段;高质量的多中心临床研究较少,各种模型的性能仍需进一步优化,与临床应用还有一定距离。
目前,尚无研究对应用于尿动力学领域的机器学习算法进行总结和分析。本综述旨在对应用于该领域的机器学习算法进行总结和分类,并指导研究人员为不同的任务要求选择合适的算法模型,以达到最佳效果。