摘要
由一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。据《中华人民共和国北京消息》，NewsRx记者报道，“机器学习算法作为一种研究工具，包括传统的机器学习和深度学习，正在创造性地应用于泌尿动力学领域。然而，没有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。“我们的新闻记者从泌尿系获得了这项研究的引文，”我们进行了叙述性回顾，评估了已发表的文献如何报道机器学习在尿动力学中的应用。我们搜索了PubMed，直到2023年12月，仅限于英语。我们选择了以下搜索词：人工智能，机器学习，本文从深学习、尿动力学、下尿路症状三个方面进行了综述，分别是尿动力学检查的应用、尿动力学相关功能障碍诊断的应用和预后预测的应用。机器学习算法在尿动力学领域的应用主要可分为三个方面：尿动力学检查、尿路功能障碍的诊断及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泛化能力，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。各种模型的性能还有待进一步优化，与临床应用还存在一定差距，目前还没有对机器学习算法在泌尿动力学领域的应用进行总结和分析的研究。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news originating from Beijing, People's Republic of Chi na, by NewsRx correspondents, research stated, "Machine learning algorithms as a research tool, including traditional machine learning and deep learning, are in creasingly applied to the field of urodynamics. However, no studies have evaluat ed how to select appropriate algorithm models for different urodynamic research tasks."Our news journalists obtained a quote from the research from the Department of U rology, "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, urodyna mics, and lower urinary tract symptoms. We identified three domains for assessme nt in advance of commencing the review. These were the applications of urodynami c studies examination, applications of diagnoses of dysfunction related to urody namics, and applications of prognosis prediction. The machine learning algorithm applied in the field of urodynamics can be mainly divided into three aspects, w hich are urodynamic examination, diagnosis of urinary tract dysfunction and pred iction of the efficacy of various treatment methods. Most of these studies were single-center retrospective studies, lacking external validation, requiring furt her validation of model generalization ability, and insufficient sample size. Th e relevant research in this field is still in the preliminary exploration stage; there are few high-quality multicenter clinical studies, and the performance o f various models still needs to be further optimized, and there is still a dista nce from clinical application. At present, there is no research to summarize and analyze the machine learning algorithms applied in the field of urodynamics."
NETL
NSTL