AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context
Summary: 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 po...
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Elsevier
2025-07-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396425001999 |
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| author | Juan Chen Jiahui Yao Wei Chen Feng Zhang Heyuan Wang Xiaoying 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 Anjing Ge Cuiping Jin Zimu Fu Qin Liu Xuefeng Yu Chengmao Zhao Tengjiao Wang Lan Zhu |
| author_facet | Juan Chen Jiahui Yao Wei Chen Feng Zhang Heyuan Wang Xiaoying 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 Anjing Ge Cuiping Jin Zimu Fu Qin Liu Xuefeng Yu Chengmao Zhao Tengjiao Wang Lan Zhu |
| author_sort | Juan Chen |
| collection | DOAJ |
| description | Summary: 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: In the external validation dataset, the area under the receiver operating characteristic curve (AUC) of the AI-Reference ranges was 11% higher than that of the Glazer standard. Specifically, the AUC values for the AI-Reference ranges on the internal and external validation datasets were 0.81 (95% CI: 8.13 × 10−1, 8.16 × 10−1) and 0.79 (95% CI: 7.90 × 10−1, 7.94 × 10−1), respectively, surpassing the AUC values of the Glazer standard of 0.76 (95% CI: 7.56 × 10−1, 7.59 × 10−1) and 0.68 (95% CI: 6.74 × 10−1, 6.78 × 10−1). Furthermore, the AI-Diagnostician-PFD model demonstrated superior diagnostic performance for PFDs, achieving an AUC 1% higher than other classical machine learning and deep learning models. Interpretation: The performance of the reference interval derived by AI surpassed that of the Glazer standard. Upon publication of this study, the AI-Diagnostician-PFD model for PFD prediction will be provided free via software on machines. Implementing this algorithm in clinical practice can enhance individual PFD diagnosis and improve population-level health outcomes. Funding: This study was supported by grants from the National Key R&D Program of China: The Establishment of a Comprehensive Network for PFD Prevention, Rehabilitation, Pelvic Floor Surgery and Related Complications (2021YFC2701300), Perception and Analysis of the Situation of Major Infectious Disease Outbreaks Based on Internet Big Data (2021ZD0111202), Research on New Models for Forecasting Major Infectious Diseases and Policy Evaluation (2021ZD0111205); Beijing Natural Science Foundation (7212073), National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-087) and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M- C&T-B- 021). |
| format | Article |
| id | doaj-art-caf6bc20d8684568bce6ca44e9ac5315 |
| institution | OA Journals |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj-art-caf6bc20d8684568bce6ca44e9ac53152025-08-20T02:30:54ZengElsevierEBioMedicine2352-39642025-07-0111710575510.1016/j.ebiom.2025.105755AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in contextJuan Chen0Jiahui Yao1Wei Chen2Feng Zhang3Heyuan Wang4Xiaoying Xu5Huan Ge6Hongmei Zhou7Jin Cen8Dan Li9Bengui Jiang10Li He11Tingting Fu12Zhengxian Xu13Lei Chu14Shuxia Zhang15Dongmei Yao16Linyi Wei17Liu Huang18Anjing Ge19Cuiping Jin20Zimu Fu21Qin Liu22Xuefeng Yu23Chengmao Zhao24Tengjiao Wang25Lan Zhu26Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, ChinaInstitute of Social Science Survey, Peking University, Beijing, China; Institute of Computational Social Science, Peking University, Qingdao, ChinaSchool of Computer Science, Peking University, Beijing, China; Institute of Computational Social Science, Peking University, Qingdao, ChinaSchool of Computer Science, Peking University, Beijing, China; Institute of Computational Social Science, Peking University, Qingdao, ChinaSchool of Computer Science, Peking University, Beijing, China; Institute of Computational Social Science, Peking University, Qingdao, ChinaGansu Maternal and Child Health Hospital, Lanzhou, ChinaJiangsu Province Hospital, Nanjing, ChinaAnhui Women and Children's Medical Center, Hefei, ChinaNinghai Maternal and Child Health Hospital, Ningbo, ChinaXi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, ChinaNingbo Women and Children's Hospital, Ningbo, ChinaChengdu Women's and Children's Central Hospital, Chengdu, ChinaQingdao Women and Children's Hospital, Qingdao, ChinaFoshan Women and Children Hospital, Foshan, ChinaThe International Peace Maternity & Child Health Hospital of China Welfare Institute, Shanghai, ChinaWeifang Maternal and Child Health Hospital, Weifang, ChinaMaternal and Child Health Hospital of Hubei Province, Wuhan, ChinaMaternity and Child Health Care of Guangxi Zhuang Autonomous Region, Nanjing, ChinaGuangzhou Women and Children's Medical Center, Guangzhou, ChinaLanzhou Maternal and Child Health Care Hospital, Lanzhou, ChinaThe Second Hospital of Tianjin Medical University, Tianjin, ChinaShaoxing Women and Children's Hospital, Shaoxing, ChinaHunan Provincial People's Hospital, Changsha, ChinaCixi Maternaity and Child Health Care Hospital, Ningbo, ChinaQinghai Maternal and Child Health Hospital, Xining, ChinaSchool of Computer Science, Peking University, Beijing, China; Institute of Computational Social Science, Peking University, Qingdao, China; Corresponding author.Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College; National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, China; Corresponding author.Summary: 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: In the external validation dataset, the area under the receiver operating characteristic curve (AUC) of the AI-Reference ranges was 11% higher than that of the Glazer standard. Specifically, the AUC values for the AI-Reference ranges on the internal and external validation datasets were 0.81 (95% CI: 8.13 × 10−1, 8.16 × 10−1) and 0.79 (95% CI: 7.90 × 10−1, 7.94 × 10−1), respectively, surpassing the AUC values of the Glazer standard of 0.76 (95% CI: 7.56 × 10−1, 7.59 × 10−1) and 0.68 (95% CI: 6.74 × 10−1, 6.78 × 10−1). Furthermore, the AI-Diagnostician-PFD model demonstrated superior diagnostic performance for PFDs, achieving an AUC 1% higher than other classical machine learning and deep learning models. Interpretation: The performance of the reference interval derived by AI surpassed that of the Glazer standard. Upon publication of this study, the AI-Diagnostician-PFD model for PFD prediction will be provided free via software on machines. Implementing this algorithm in clinical practice can enhance individual PFD diagnosis and improve population-level health outcomes. Funding: This study was supported by grants from the National Key R&D Program of China: The Establishment of a Comprehensive Network for PFD Prevention, Rehabilitation, Pelvic Floor Surgery and Related Complications (2021YFC2701300), Perception and Analysis of the Situation of Major Infectious Disease Outbreaks Based on Internet Big Data (2021ZD0111202), Research on New Models for Forecasting Major Infectious Diseases and Policy Evaluation (2021ZD0111205); Beijing Natural Science Foundation (7212073), National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-087) and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M- C&T-B- 021).http://www.sciencedirect.com/science/article/pii/S2352396425001999Pelvic floor dysfunctionsSurface electromyographyArtificial intelligenceReference rangesPelvic floor muscle |
| spellingShingle | Juan Chen Jiahui Yao Wei Chen Feng Zhang Heyuan Wang Xiaoying 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 Anjing Ge Cuiping Jin Zimu Fu Qin Liu Xuefeng Yu Chengmao Zhao Tengjiao Wang Lan Zhu AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context EBioMedicine Pelvic floor dysfunctions Surface electromyography Artificial intelligence Reference ranges Pelvic floor muscle |
| title | AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context |
| title_full | AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context |
| title_fullStr | AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context |
| title_full_unstemmed | AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context |
| title_short | AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context |
| title_sort | ai based pelvic floor surface electromyography reference ranges and high precision pelvic floor dysfunction diagnosisresearch in context |
| topic | Pelvic floor dysfunctions Surface electromyography Artificial intelligence Reference ranges Pelvic floor muscle |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425001999 |
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