机器学习在尿动力学中的应用:综述。

IF 1.8 3区 医学 Q3 UROLOGY & NEPHROLOGY
Neurourology and Urodynamics Pub Date : 2024-09-01 Epub Date: 2024-06-04 DOI:10.1002/nau.25490
Xin Liu, Ping Zhong, Yi Gao, Limin Liao
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引用次数: 0

摘要

背景: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,正越来越多地应用于尿动力学领域。然而,还没有研究对如何为不同的尿动力学研究任务选择合适的算法模型进行评估:我们对已发表的文献如何报道机器学习在尿动力学中的应用进行了叙述性综述评估。我们检索了截至 2023 年 12 月的 PubMed,仅限于英语。我们选择了以下检索词: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、尿动力学相关功能障碍诊断的应用以及预后预测的应用:在尿动力学领域应用的机器学习算法主要分为三个方面,即尿动力学检查、尿路功能障碍诊断和各种治疗方法的疗效预测。这些研究大多为单中心回顾性研究,缺乏外部验证,模型的泛化能力需要进一步验证,样本量也不足。该领域的相关研究还处于初步探索阶段,高质量的多中心临床研究较少,各种模型的性能还有待进一步优化,离临床应用还有一定距离:目前,还没有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。本综述旨在对该领域应用的机器学习算法进行总结和分类,指导研究人员针对不同的任务要求选择合适的算法模型,以达到最佳效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of machine learning in urodynamics: A narrative review.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
Neurourology and Urodynamics
Neurourology and Urodynamics 医学-泌尿学与肾脏学
CiteScore
4.30
自引率
10.00%
发文量
231
审稿时长
4-8 weeks
期刊介绍: Neurourology and Urodynamics welcomes original scientific contributions from all parts of the world on topics related to urinary tract function, urinary and fecal continence and pelvic floor function.
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