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TRUVACE RECORD VERSION
record: TRV-2026-2337
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-02T21:27:56.385292Z
status: published
lens: g_space
sector: health
headline: 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
dek: 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 T…
gain_title: Over two months of home training with an AI-assisted pressure-sensing device, women with mild prolapse or stress urinary incontinence reported fewer symptoms, leaked less urine, and had better muscle contraction quality.
problem_title: (none)
trace_subject: (none)
gain_reading: Over two months of home training with an AI-assisted pressure-sensing device, women with mild prolapse or stress urinary incontinence reported fewer symptoms, leaked less urine, and had better muscle contraction quality.
gain_evidence: Real-time AI-assisted PFMT significantly improved symptom scores, objective urinary leakage, and contraction quality in women with POP or SUI.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers at Taipei Veterans General Hospital tested a wireless, noninvasive pressure-sensing device that lets women do pelvic floor muscle training at home while AI assesses each contraction in real time. Women with stage I-II pelvic organ prolapse or stress urinary incontinence were followed at baseline, one month and two months.

Pelvic floor training is the first-line conservative treatment, but poor adherence and technique often limit it, so feedback that flags faulty contractions could matter. Because the study had no comparison group, it cannot show how much of the improvement came from the AI feature itself rather than from training alone.
limitation: The authors note the study was limited by a small sample size, short follow-up and a single-arm design, so the effect of the AI assessment cannot be separated from training itself.
tag: Evidence-backed gain
key_points: 27 women, mean age 59.0 years, enrolled and all completed the two-month study. | Prolapse distress scores (POPDI-6) fell from 8.6 to 5.2 over the study. | Outcomes included urinary distress and impact questionnaires plus a 1-hour pad test for leakage. | Contraction patterns were classified as optimal or non-optimal, and the device identified dysfunctional patterns.
rundown: The device is described as a third-generation home-based trainer using a noninvasive wireless pressure sensor. Real-time AI-assisted assessment analyzes contraction patterns and categorizes them as optimal or non-optimal during use.

The study was a prospective single-arm trial at one hospital. Primary outcomes were the POPDI-6, UDI-6, IIQ-7 questionnaires and the 1-hour pad test, measured at baseline, 1 month and 2 months.

The authors conclude the approach improved symptom scores, objective leakage and contraction quality, and helps identify and optimize dysfunctional contraction patterns to enhance pelvic floor rehabilitation.
sources:
- peer_reviewed | Taiwanese Journal of Obstetrics and Gynecology | https://doi.org/10.1016/j.tjog.2026.08.006 | 2026-11-01
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