Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Advanced Platform Technology Center, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, Ohio, USA.
Neurourol Urodyn. 2024 Aug;43(6):1303-1310. doi: 10.1002/nau.25362. Epub 2023 Dec 27.
A session at the 2023 International Consultation on Incontinence - Research Society (ICI-RS) held in Bristol, UK, focused on the question: Is the time right for a new initiative in mathematical modeling of the lower urinary tract (LUT)? The LUT is a complex system, comprising various synergetic components (i.e., bladder, urethra, neural control), each with its own dynamic functioning and high interindividual variability. This has led to a variety of different types of models for different purposes, each with advantages and disadvantages.
When addressing the LUT, the modeling approach should be selected and sized according to the specific purpose, the targeted level of detail, and the available computational resources. Four areas were selected as examples to discuss: utility of nomograms in clinical use, value of fluid mechanical modeling, applications of models to simplify urodynamics, and utility of statistical models.
A brief literature review is provided along with discussion of the merits of different types of models for different applications. Remaining research questions are provided.
Inadequacies in current (outdated) models of the LUT as well as recent advances in computing power (e.g., quantum computing) and methods (e.g., artificial intelligence/machine learning), would dictate that the answer is an emphatic "Yes, the time is right for a new initiative in mathematical modeling of the LUT."
在英国布里斯托举行的 2023 年国际尿失禁咨询-研究学会(ICI-RS)会议上的一个分会聚焦于这样一个问题:是否是时候发起一项新的下尿路(LUT)数学建模计划了?LUT 是一个复杂的系统,包含各种协同作用的组成部分(即膀胱、尿道、神经控制),每个部分都有其自身的动态功能和高度的个体间变异性。这导致了针对不同目的的各种不同类型的模型,每种模型都有其优点和缺点。
在处理 LUT 时,应根据具体目的、目标详细程度和可用的计算资源选择和调整建模方法。选择了四个领域作为示例进行讨论:列线图在临床应用中的效用、流体力学建模的价值、模型在简化尿动力学中的应用以及统计模型的效用。
提供了简短的文献综述,并讨论了不同类型的模型在不同应用中的优缺点。还提出了尚未解决的研究问题。
LUT 现有(过时)模型的不足以及计算能力(例如,量子计算)和方法(例如,人工智能/机器学习)的最新进展表明,答案是肯定的,“现在是时候发起一项新的 LUT 数学建模计划了。”