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

2026년 5월 27일 업데이트: 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

연구 개요

상태

모병

정황

개입 / 치료

연구 유형

중재적

등록 (추정된)

1180

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

      • Changhua, 대만
        • 모병
        • Show Chwan Memorial Hospital
        • 연락하다:
      • Kaohsiung City, 대만
        • 모병
        • Kaohsiung Medical University Chung-Ho Memorial Hospital
        • 연락하다:
      • Taichung, 대만
        • 모병
        • Taichung Veterans General Hospital
        • 연락하다:

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

예

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 특수 증상
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: 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.
간섭 없음: Control Group
Participants will receive standard-of-care (routine diagnosis) for osteoporosis assessment.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
The primary objective is to evaluate the clinical effectiveness in terms of the improvement of BMD, of AI-guided diagnosis (VeriOsteo OP)
기간: 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)

2차 결과 측정

결과 측정
측정값 설명
기간
Incidence of Bone Fractures
기간: 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
기간: 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

기타 결과 측정

결과 측정
측정값 설명
기간
Osteoporosis-related healthcare resource utilization
기간: 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
기간: 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

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

수사관

  • 수석 연구원: Kun Hui Chen, MD, Taichung Veterans General Hospital

간행물 및 유용한 링크

연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.

유용한 링크

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2026년 3월 13일

기본 완료 (추정된)

2026년 6월 17일

연구 완료 (추정된)

2026년 11월 12일

연구 등록 날짜

최초 제출

2026년 5월 14일

QC 기준을 충족하는 최초 제출

2026년 5월 27일

처음 게시됨 (실제)

2026년 6월 2일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 2일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 5월 27일

마지막으로 확인됨

2026년 5월 1일

추가 정보

이 연구와 관련된 용어

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

아니요

IPD 계획 설명

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.

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

구독하다