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Artificial Intelligence-Guided Diagnosis for High-Risk Osteoporosis Populations: A Pragmatic Randomized Clinical Trial

27. Mai 2026 aktualisiert von: Kun-Hui Chen, Taichung Veterans General Hospital

Project Summary

I. Project Objectives

With the rapid advancement of medical technology, smart medical devices have become one of the key components of modern healthcare. However, integrating these emerging technologies into the national health insurance (NHI) reimbursement system while ensuring their clinical value and economic benefits remains a major challenge worldwide.

The primary goal of this project is to assist commercialized smart medical device products-those that have passed TFDA review and seek NHI reimbursement-in conducting comprehensive evaluations of their clinical effectiveness and medical economic impact. Through scientific data and standardized impact assessment procedures, the project aims to provide localized evidence to support reimbursement policy decisions and facilitate the market adoption of smart medical technologies. Ultimately, this project seeks to balance therapeutic efficacy and cost control, offering a scientific foundation for NHI decision-making and paving the way for the sustainable development of AI-driven healthcare innovations.

II. Implementation Methods

  1. Multi-center Collaborative Network

    This project will be led by Taichung Veterans General Hospital (TCVGH) as the principal site, with collaboration from Kaohsiung Medical University Chung-Ho Memorial Hospital and Changhua Show Chwan Memorial Hospital. This cross-institutional, cross-regional alliance ensures diverse clinical samples, enhances the representativeness of study results, and allows evaluation of AI medical devices across different healthcare systems and environments.

  2. Clinical Trial Design and Implementation for Smart Medical Devices

    The project will design and execute systematic clinical trials for smart medical devices using methodologies such as randomized controlled trials (RCTs), before-and-after studies, pragmatic RCTs (PCTs), cluster RCTs, and stepped-wedge RCTs. These rigorous designs will ensure scientific validity, reproducibility, and practical feasibility in real-world clinical settings.

  3. Health Economic Evaluation

    A key component of this project is the medical economic assessment, conducted by experienced health economists through cost-effectiveness analysis. The evaluation will focus on how smart medical devices reduce healthcare costs, improve diagnostic efficiency, and enhance treatment outcomes, quantifying their economic value within the NHI system. This evidence will guide policy makers in making data-driven reimbursement decisions.

  4. Standardized Impact Assessment Process

    To ensure high-quality research, the project will establish a comprehensive standardized impact assessment framework covering trial design, data collection, statistical analysis, economic evaluation, and ethical review. This standardized approach not only improves the precision of the study but also accelerates the clinical translation of AI medical devices through streamlined application and review processes.

  5. Research Case Study and Clinical Application

    The featured case study in this project is "VeriOsteo OP® Smart Bone Screening System," which targets early osteoporosis screening among adults aged 40 to 80 years in high-risk groups. This study will evaluate the clinical accuracy of AI-based osteoporosis screening and assess its economic contribution to healthcare cost reduction. The findings will directly inform the Ministry of Health and Welfare's NHI Administration in formulating reimbursement standards for AI medical devices.

  6. Data Sharing and Information Security

    Throughout the project, all research data collection, exchange, and sharing will strictly adhere to cybersecurity and privacy regulations. The AI models involved will undergo validation to ensure the reliability and scientific rigor of the results. Furthermore, the project will promote collaboration between manufacturers and healthcare institutions to support the adoption of smart medical technologies.

  7. Final Outcomes and Future Development

The final deliverables will include a comprehensive evaluation report on the clinical and economic performance of the AI medical device, along with recommendations for NHI reimbursement application. The results will provide a reference model for future AI medical devices entering the reimbursement system and further advance the field of smart healthcare. In the long term, the center aims to expand its research to other disease domains while strengthening data security and ethical oversight to ensure the feasibility and credibility of AI applications in clinical practice.

III. Expected Outcomes and Future Vision

By implementing a multi-center collaborative framework, this project aims to promote clinical adoption and economic evaluation of smart medical devices, offering concrete data to support NHI policy-making. Over time, the project is expected to establish a robust evaluation system for AI medical devices, facilitate broader market adoption, and enhance patient outcomes whi

Studienübersicht

Status

Rekrutierung

Bedingungen

Intervention / Behandlung

Studientyp

Interventionell

Einschreibung (Geschätzt)

1180

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

      • Changhua, Taiwan
        • Rekrutierung
        • Show Chwan Memorial Hospital
        • Kontakt:
      • Kaohsiung City, Taiwan
        • Rekrutierung
        • Kaohsiung Medical University Chung-Ho Memorial Hospital
        • Kontakt:
      • Taichung, Taiwan
        • Rekrutierung
        • Taichung Veterans General Hospital
        • Kontakt:

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Ja

Beschreibung

Inclusion Criteria:

  • Aged between 40 to 80 years old
  • Identified as high-risk by the Osteoporosis Self-Assessment Tool for Taiwan Postmenopausal Women (OSTAi) <-1 or Male Osteoporosis Self-Assessment Tool for Taiwan (MOSTAi) ≦11 based on the individual's age and weight (kg)
  • Had chest x-ray within one year

Exclusion Criteria:

  • Age < 40 years old
  • Age >80 years old
  • BMI< 18 kg/m2or >30 kg/m2
  • Pregnant in prior one year
  • Recorded diagnosis of osteoporosis within the past two years
  • History of prior DXA imaging (prior quantification of BMD) within 2 years
  • History of metabolic bone disease

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Diagnose
  • Zuteilung: Zufällig
  • Interventionsmodell: Parallele Zuordnung
  • Maskierung: Single

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Experimental: Intervention Group
Participants will receive AI-guided diagnosis for osteoporosis risk assessment. This group follows a 2:1 randomization ratio.
Participants in the intervention group undergo VeriOsteo OP AI assessment followed by DXA confirmation. The AI system generates a diagnostic report identifying individuals at high risk for osteoporosis. Results are used to guide clinical decision-making and evaluate the diagnostic consistency between the AI-guided system and the gold-standard DXA assessment.
Kein Eingriff: Control Group
Participants will receive standard-of-care (routine diagnosis) for osteoporosis assessment.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
The primary objective is to evaluate the clinical effectiveness in terms of the improvement of BMD, of AI-guided diagnosis (VeriOsteo OP)
Zeitfenster: Up to the end of follow-up (average 18 months)
The primary endpoint of this study is the change in Bone Mineral Density (BMD) expressed as a T-score, measured by Dual-energy X-ray Absorptiometry (DXA). The T-score is a standardized metric comparing bone density to a healthy young adult. While there is no theoretical absolute minimum or maximum value, typical clinical values range from -5.0 to +2.0. According to the International Osteoporosis Foundation (IOF) and Taiwan 2020 guidelines, a T-score of -1.0 or higher is normal, between -1.0 and -2.5 indicates osteopenia, and a T-score of -2.5 or lower (≤ -2.5) indicates osteoporosis. Therefore, a higher (more positive) T-score represents a better outcome (higher bone density). To avoid inflating the detection rate for osteoporosis based on the protocolized DXA at baseline in the intervention arm, a positive AI screen was required to be present to count the chest X-ray findings for the purpose of the primary end point.
Up to the end of follow-up (average 18 months)

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Incidence of Bone Fractures
Zeitfenster: Every 6 months up to 18 months
he incidence of bone fractures, including hip fracture, vertebral fracture, wrist fracture, and other fractures. Bone fractures will be identified from the participant interviews and confirmed by International Classification of Diseases (ICD) codes extracted from electronic health records (EHRs) across the three study sites.
Every 6 months up to 18 months
All-Cause Mortality
Zeitfenster: Every 6 months up to 18 months
The incidence of all-cause death. Participant status will be followed by telephone and verified through electronic health records (EHRs) from the three study sites.
Every 6 months up to 18 months

Andere Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Osteoporosis-related healthcare resource utilization
Zeitfenster: 12th month
To analyze the total number of healthcare resource utilizations related to osteoporosis, including the combined counts of outpatient visits, emergency department visits, and hospitalizations. Data will be collected through a structured questionnaire via telephone interview.
12th month
Osteoporosis-related medical costs
Zeitfenster: 12th month
To evaluate the direct medical costs associated with osteoporosis healthcare resource utilization (expressed in New Taiwan Dollars (TWD ). Data will be collected through a structured questionnaire via telephone interview.
12th month

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Ermittler

  • Hauptermittler: Kun Hui Chen, MD, Taichung Veterans General Hospital

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Nützliche Links

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Tatsächlich)

13. März 2026

Primärer Abschluss (Geschätzt)

17. Juni 2026

Studienabschluss (Geschätzt)

12. November 2026

Studienanmeldedaten

Zuerst eingereicht

14. Mai 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

27. Mai 2026

Zuerst gepostet (Tatsächlich)

2. Juni 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

2. Juni 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

27. Mai 2026

Zuletzt verifiziert

1. Mai 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

NEIN

Beschreibung des IPD-Plans

As this is a multi-center study, the sharing of individual participant data (IPD) would require joint approval from the Institutional Review Boards (IRBs) of all three participating hospitals. Given the complex regulatory procedures and strict privacy protocols involved, the data will not be made publicly available at this time.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .

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