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
調査の概要
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前:WU WU ZHAN, M.B.A.
- 電話番号:04-23592525#3973
- メール:tcvghaicenter@vghtc.gov.tw
研究場所
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Changhua、台湾
- 募集
- Show Chwan Memorial Hospital
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コンタクト:
- Chu Pei-Yi, MD
- 電話番号:0975611855
- メール:chu.peiyi@msa.hinet.net
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Kaohsiung City、台湾
- 募集
- Kaohsiung Medical University Chung-Ho Memorial Hospital
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コンタクト:
- Jaw Yuan Wang, MD
- 電話番号:07-3121101
- メール:1030771@kmuh.org.tw
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Taichung、台湾
- 募集
- Taichung Veterans General Hospital
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コンタクト:
- Kun Hui Chen, MD
- 電話番号:04-23592525#5100
- メール:khc@vghtc.gov.tw
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:診断
- 割り当て:ランダム化
- 介入モデル:並列代入
- マスキング:独身
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的: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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介入なし:Control Group
Participants will receive standard-of-care (routine diagnosis) for osteoporosis assessment.
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この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
The primary objective is to evaluate the clinical effectiveness in terms of the improvement of BMD, of AI-guided diagnosis (VeriOsteo OP)
時間枠: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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Incidence of Bone Fractures
時間枠: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
時間枠: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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その他の成果指標
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Osteoporosis-related healthcare resource utilization
時間枠: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
時間枠: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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協力者と研究者
捜査官
- 主任研究者:Kun Hui Chen, MD、Taichung Veterans General Hospital
出版物と役立つリンク
便利なリンク
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- SC251020B
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。