Artificial Intelligence-Guided Diagnosis for High-Risk Osteoporosis Populations: A Pragmatic Randomized Clinical Trial
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
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.
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.
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.
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.
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.
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.
- 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
研究概览
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名:WU WU ZHAN, M.B.A.
- 电话号码:04-23592525#3973
- 邮箱:tcvghaicenter@vghtc.gov.tw
学习地点
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Changhua、台湾
- 招聘中
- Show Chwan Memorial Hospital
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接触:
- Chu Pei-Yi, MD
- 电话号码:0975611855
- 邮箱:chu.peiyi@msa.hinet.net
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Kaohsiung City、台湾
- 招聘中
- Kaohsiung Medical University Chung-Ho Memorial Hospital
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接触:
- Jaw Yuan Wang, MD
- 电话号码:07-3121101
- 邮箱:1030771@kmuh.org.tw
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Taichung、台湾
- 招聘中
- Taichung Veterans General Hospital
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接触:
- Kun Hui Chen, MD
- 电话号码:04-23592525#5100
- 邮箱:khc@vghtc.gov.tw
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参与标准
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
描述
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
学习计划
研究是如何设计的?
设计细节
- 主要用途:诊断
- 分配:随机化
- 介入模型:并行分配
- 屏蔽:单身的
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
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实验性的:Intervention Group
Participants will receive AI-guided diagnosis for osteoporosis risk assessment.
This group follows a 2:1 randomization ratio.
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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.
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无干预:Control Group
Participants will receive standard-of-care (routine diagnosis) for osteoporosis assessment.
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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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The primary objective is to evaluate the clinical effectiveness in terms of the improvement of BMD, of AI-guided diagnosis (VeriOsteo OP)
大体时间:Up to the end of follow-up (average 18 months)
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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.
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Up to the end of follow-up (average 18 months)
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Incidence of Bone Fractures
大体时间:Every 6 months up to 18 months
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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.
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Every 6 months up to 18 months
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All-Cause Mortality
大体时间:Every 6 months up to 18 months
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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.
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Every 6 months up to 18 months
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其他结果措施
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Osteoporosis-related healthcare resource utilization
大体时间:12th month
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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.
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12th month
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Osteoporosis-related medical costs
大体时间:12th month
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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.
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12th month
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合作者和调查者
调查人员
- 首席研究员:Kun Hui Chen, MD、Taichung Veterans General Hospital
出版物和有用的链接
有用的网址
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- SC251020B
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)?
IPD 计划说明
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