AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosis

作者：Juan Chen, Jiahui Yao, Wei Chen, Feng Zhang, Heyuan Wang, Xiao‐Ying Xu, Huan Ge, Hongmei Zhou, Jin Cen, Dan Li, Bengui Jiang, Li He, Tingting Fu, Zhengxian Xu, Lei Chu, Shuxia Zhang, Dongmei Yao, Linyi Wei, Liu Huang, Allègre Ge, Cuiping Jin, Zimu Fu, Qin Liu, Xue‐Feng Yu, Chengmao Zhao, Tengjiao Wang, Lan Zhu · 发表于：EBioMedicine · 年份：2025 · DOI：10.1016/j.ebiom.2025.105755 · 被引用次数：7 · 研究领域：Pelvic floor disorders treatments、Preterm Birth and Chorioamnionitis、Gastrointestinal motility and disorders

BACKGROUND: Pelvic floor surface electromyography (sEMG) is widely used to evaluate and treat pelvic floor dysfunctions (PFDs). Based on sEMG, the Glazer protocol was developed over 20 years ago with a limited sample size, making it challenging to accurately diagnose PFDs across diverse populations and conditions. This study aims to establish a multidimensional database for monitoring pelvic floor sEMG, derive more reasonable reference ranges for sEMG parameters, and achieve accurate diagnosis of PFDs through artificial intelligence (AI). METHODS: In this population-based, multicenter, cross-sectional study, we recruited 1605 participants from 21 centres across China, collected pelvic floor sEMG data, and established a multidimensional sEMG database. Based on the database, we developed an AI-Diagnostician-PFD diagnostic model, which leverages AI to derive AI-Reference ranges for sEMG parameters and diagnose PFDs. Data from 15 centres were divided into a training dataset (60%) and a test dataset (40%), while data from 6 additional centres were used to form an independent validation dataset. The proportions of normal and abnormal samples were consistent across the 15 and 6 centres, ensuring balanced representation. Additionally, both datasets encompassed diverse geographical regions, enhancing the model's generalizability. The diagnostic performance of the AI-Diagnostician-PFD model was evaluated on both the internal test dataset and the external validation dataset. FINDINGS: )...