Wang Qi, Wang Xiaoxiao, Jiang Xiaoxiang, Lin Chaoqin
College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Department of Gynecology, Fujian Maternity and Child Health Hospital, Fuzhou, China.
Digit Health. 2024 Oct 7;10:20552076241281450. doi: 10.1177/20552076241281450. eCollection 2024 Jan-Dec.
The aim was to conduct a scoping review of the literature on the use of machine learning (ML) in female urinary incontinence (UI) over the last decade.
A systematic search was performed among the Medline, Google Scholar, PubMed, and Web of Science databases using the following keywords: [Urinary incontinence] and [(Machine learning) or (Predict) or (Prediction model)]. Eligible studies were considered to have applied ML model to explore different management processes of female UI. Data analyzed included the field of application, type of ML, input variables, and results of model validation.
A total of 798 papers were identified while 23 finally met the inclusion criteria. The vast majority of studies applied logistic regression to establish models (91.3%, 21/23). Most frequently ML was applied to predict postpartum UI (39.1%, 9/23), followed by de novo incontinence after pelvic floor surgery (34.8%, 8/23).There are also three papers using ML models to predict treatment outcomes and three papers using ML models to assist in diagnosis. Variables for modeling included demographic characteristics, clinical data, pelvic floor ultrasound, and urodynamic parameters. The area under receiver operating characteristic curve of these models fluctuated from 0.56 to 0.95, and only 11 studies reported sensitivity and specificity, with sensitivity ranging from 20% to 96.2% and specificity from 59.8% to 94.5%.
Machine learning modeling demonstrated good predictive and diagnostic abilities in some aspects of female UI, showing its promising prospects in near future. However, the lack of standardization and transparency in the validation and evaluation of the models, and the insufficient external validation greatly diminished the applicability and reproducibility, thus a focus on filling this gap is strongly recommended for future research.
目的是对过去十年中机器学习（ML）在女性尿失禁（UI）中的应用相关文献进行范围综述。
在Medline、谷歌学术、PubMed和科学网数据库中进行系统检索，使用以下关键词：[尿失禁]和[(机器学习)或(预测)或(预测模型)]。符合条件的研究被认为是应用了ML模型来探索女性UI的不同管理流程。分析的数据包括应用领域、ML类型、输入变量和模型验证结果。
共识别出798篇论文，最终23篇符合纳入标准。绝大多数研究应用逻辑回归建立模型（91.3%，21/23）。ML最常应用于预测产后UI（39.1%，9/23），其次是盆底手术后的新发尿失禁（34.8%，8/23）。也有三篇论文使用ML模型预测治疗结果，三篇论文使用ML模型辅助诊断。建模变量包括人口统计学特征、临床数据、盆底超声和尿动力学参数。这些模型的受试者操作特征曲线下面积在0.56至0.95之间波动，只有11项研究报告了敏感性和特异性，敏感性范围为20%至96.2%，特异性范围为59.8%至94.5%。
机器学习建模在女性UI的某些方面表现出良好的预测和诊断能力，显示出其在不久的将来有广阔前景。然而，模型验证和评估中缺乏标准化和透明度，以及外部验证不足，大大降低了其适用性和可重复性，因此强烈建议未来研究关注填补这一空白。
