Real-time artificial intelligence-assisted assessment in a home-based pelvic floor muscle training device for pelvic organ prolapse and stress urinary incontinence: A single-center prospective study
- 1Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of BioMedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan; Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- 2Department of Information Engineering and Computer Science, Tamkang University, New Taipei City, Taiwan
- 3Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan
Abstract
Objective: Pelvic floor muscle training (PFMT) is the first-line conservative treatment of pelvic organ prolapse (POP) and stress urinary incontinence (SUI), but its effectiveness is often limited by poor adherence and performance. We developed a third-generation home-based PFMT device incorporating real-time artificial intelligence (AI)-assisted assessment. This study aimed to evaluate the efficacy of this device in women with POP or SUI. Materials and methods: This prospective single-arm study was conducted at Taipei Veterans General Hospital. Women with stage I–II POP or SUI performed home-based PFMT using a noninvasive wireless pressure-sensing device with real-time AI-assisted assessment. Outcomes were assessed at baseline (M0), 1 (M1), and 2 (M2) months. Primary outcomes included the Pelvic Organ Prolapse Distress Inventory-6 (POPDI-6), Urogenital Distress Inventory-6 (UDI-6), Incontinence Impact Questionnaire-7 (IIQ-7), and the 1-h pad test. Contraction patterns were analyzed and categorized as optimal or non-optimal. Results: A total of 27 women (mean age 59.0 ± 14.3 years) were enrolled and completed the study. POPDI-6 decreased from 8.6 ± 2.6 to 5.2 ± 3.0 ( p < 0.01), and UDI-6 from 6.4 ± 4.9 to 3.4 ± 3.4 ( p < 0.05). The 1-h pad test decreased from 30.9 ± 41.9 g to 14.3 ± 23.5 g ( p < 0.01); IIQ-7 reduction was also significant. Subjective and objective improvement rates were 89% and 54%, respectively. Physician-directed pattern optimization significantly improved contraction patterns, with 52.9% of patients converting from non-optimal to optimal patterns and increased the optimal contraction rate from 37.0% to 70.4% ( p < 0.01). Conclusion: Real-time AI-assisted PFMT significantly improved symptom scores, objective urinary leakage, and contraction quality in women with POP or SUI. While limited by a small sample size, a short follow-up period, and a single-arm design, this approach effectively identifies and optimizes dysfunctional contraction patterns to enhance pelvic floor rehabilitation.
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BibTeX
@article{horng2026realtime,
title = {{Real-time artificial intelligence-assisted assessment in a home-based pelvic floor muscle training device for pelvic organ prolapse and stress urinary incontinence: A single-center prospective study}},
author = {Huann-Cheng Horng and Jui-Fa Chen and Chia-Pei Chang and Wei-Ting Chao},
journal = {Taiwanese Journal of Obstetrics \& Gynecology},
year = {2026},
month = oct,
doi = {10.1016/j.tjog.2026.08.006},
url = {https://doi.org/10.1016/j.tjog.2026.08.006},
note = {CC BY},
}
RIS
TY - JOUR
TI - Real-time artificial intelligence-assisted assessment in a home-based pelvic floor muscle training device for pelvic organ prolapse and stress urinary incontinence: A single-center prospective study
AU - Huann-Cheng Horng
AU - Jui-Fa Chen
AU - Chia-Pei Chang
AU - Wei-Ting Chao
PY - 2026
DA - 2026/10/31
JO - Taiwanese Journal of Obstetrics & Gynecology
DO - 10.1016/j.tjog.2026.08.006
UR - https://doi.org/10.1016/j.tjog.2026.08.006
AB - Objective: Pelvic floor muscle training (PFMT) is the first-line conservative treatment of pelvic organ prolapse (POP) and stress urinary incontinence (SUI), but its effectiveness is often limited by poor adherence and performance. We developed a third-generation home-based PFMT device incorporating real-time artificial intelligence (AI)-assisted assessment. This study aimed to evaluate the efficacy of this device in women with POP or SUI. Materials and methods: This prospective single-arm study was conducted at Taipei Veterans General Hospital. Women with stage I–II POP or SUI performed home-based PFMT using a noninvasive wireless pressure-sensing device with real-time AI-assisted assessment. Outcomes were assessed at baseline (M0), 1 (M1), and 2 (M2) months. Primary outcomes included the Pelvic Organ Prolapse Distress Inventory-6 (POPDI-6), Urogenital Distress Inventory-6 (UDI-6), Incontinence Impact Questionnaire-7 (IIQ-7), and the 1-h pad test. Contraction patterns were analyzed and categorized as optimal or non-optimal. Results: A total of 27 women (mean age 59.0 ± 14.3 years) were enrolled and completed the study. POPDI-6 decreased from 8.6 ± 2.6 to 5.2 ± 3.0 ( p < 0.01), and UDI-6 from 6.4 ± 4.9 to 3.4 ± 3.4 ( p < 0.05). The 1-h pad test decreased from 30.9 ± 41.9 g to 14.3 ± 23.5 g ( p < 0.01); IIQ-7 reduction was also significant. Subjective and objective improvement rates were 89% and 54%, respectively. Physician-directed pattern optimization significantly improved contraction patterns, with 52.9% of patients converting from non-optimal to optimal patterns and increased the optimal contraction rate from 37.0% to 70.4% ( p < 0.01). Conclusion: Real-time AI-assisted PFMT significantly improved symptom scores, objective urinary leakage, and contraction quality in women with POP or SUI. While limited by a small sample size, a short follow-up period, and a single-arm design, this approach effectively identifies and optimizes dysfunctional contraction patterns to enhance pelvic floor rehabilitation.
SN - 1028-4559
ER -