{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department 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",
    "Department of Information Engineering and Computer Science, Tamkang University, New Taipei City, Taiwan",
    "Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Obstetrics and Gynecology, National Yang Ming Chiao Tung University, Taipei, Taiwan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huann-Cheng Horng"
    },
    {
      "affs": [
        1
      ],
      "name": "Jui-Fa Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Chia-Pei Chang"
    },
    {
      "name": "Wei-Ting Chao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5tjzcdxe4s4se.bib",
  "cited_by": 0,
  "doi": "10.1016/j.tjog.2026.08.006",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1028455926002809",
  "id": "5tjzcdxe4s4se",
  "issns": [
    "1028-4559"
  ],
  "landing_url": "https://doi.org/10.1016/j.tjog.2026.08.006",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RG1-991"
    ],
    [
      "openalex",
      "2745|Pelvic floor disorders treatments"
    ]
  ],
  "openalex": "W7224093397",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-31",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/5tjzcdxe4s4se.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:22:44Z",
      "native_id": "0e54e886031c46ab9fee7b8172fc18e7",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.tjog.2026.08.006"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RG1-991",
      "subfield": "reproductive-child"
    }
  ],
  "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",
  "title_text": "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",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T14:20:43Z",
  "url": "https://research.draconitic.com/paper/5tjzcdxe4s4se",
  "venue": "Taiwanese Journal of Obstetrics & Gynecology",
  "versions": []
}