응급 치료실의 AI 지원 골절 감지 평가 (FAIR)
정형외과 응급 치료실에서 AI 지원 골절 감지의 비용 효율성 및 작업 흐름 영향 평가
This study evaluates whether artificial intelligence (AI) can support doctors who interpret X-rays for suspected fractures in emergency care.
In the participating hospitals, X-rays are usually interpreted first by the frontline treating physician, while the formal radiology report is generally available later and not before the patient leaves the emergency department. AI may therefore provide an immediate additional assessment while clinical decisions are being made.
Patients were randomly assigned to one of two groups. In the AI-assisted group, physicians interpreted the X-rays with support from an AI system. In the control group, physicians interpreted the same type of X-rays without AI support. All final diagnoses and treatment decisions remained with the treating physician.
The main question is whether AI assistance affects the time from emergency department triage to discharge. The study also evaluates whether AI influences physician diagnostic confidence, the use of additional imaging, missed fractures, and diagnostic accuracy.
The study included patients aged 2 years or older presenting with an isolated musculoskeletal injury or relevant joint complaint requiring X-ray imaging. No additional imaging or treatment was required solely because of study participation.
연구 개요
상세 설명
The FAIR (Fracture detection with AI in emergency Radiography) Trial was a prospective, international, multicentre, pragmatic randomised controlled trial evaluating the clinical impact of artificial intelligence (AI)-assisted interpretation of musculoskeletal radiographs in routine emergency care. The study was conducted across four clinical units at three hospitals in Austria and Germany.
Clinical workflow and randomisation
In the participating departments, radiographs were interpreted by frontline orthopaedic trauma or paediatric physicians, who made immediate diagnostic and treatment decisions. Formal radiology reports were generally available later. The intervention therefore evaluated AI as an additional decision-support tool rather than as a replacement for physician interpretation. Patient encounters were individually randomised using a single sequence generated by the trial statistician. Allocation was concealed using folded, sequentially numbered study forms and revealed after radiography. Patients were then directed to designated AI-assisted or control treatment areas. All clinical decisions remained the responsibility of the treating physician.
AI implementation
The intervention used BoneView version 2.3.8 (Gleamer, Paris, France), which provided visual annotations and classifications for fractures, focal bone lesions, dislocations, and joint effusions. The software version remained unchanged throughout recruitment. At Salzburg and Hallein, BoneView was delivered through Aidoc aiOS version 3.24.0; at Nuremberg, it was integrated through Visage version 7.1.19. Participating physicians received standardised training before using the system. AI output was documented on study case-report forms, although detailed software telemetry on actual viewing behaviour was unavailable.
Protocol evolution
During recruitment, an exploratory unblinded analysis in October 2025 examined prospectively collected clinical and workflow outcomes. Following methodological feedback during the Austrian Science Fund review process in March 2026, the outcome hierarchy for the fracture-focused clinical-effectiveness analysis was revised in consultation with the trial statistician, prioritising time from emergency department triage to discharge over diagnostic accuracy. The amendment did not alter randomisation, clinical procedures, or data collection. The originally registered cost-efficiency outcome was discontinued because the available data did not permit a reliable health-economic evaluation.
Reference standard
For fracture-related diagnostic analyses, a radiologist experienced in musculoskeletal imaging and a senior orthopaedic trauma surgeon reviewed the index radiographs and available clinical information. Disagreements were resolved by consensus or, when necessary, by a third expert. The assessment distinguished fractures visible on the original radiographs from fractures detectable only on subsequent imaging.
연구 유형
등록 (실제)
단계
- 해당 없음
연락처 및 위치
참여기준
자격 기준
공부할 수 있는 나이
건강한 자원 봉사자를 받아들입니다
연구 대상 성별
설명
Inclusion Criteria:
- Age 2 years or older.
- Presentation to the emergency department following trauma or relevant joint complaint requiring plain radiographic imaging.
- Injury involving a single anatomical region within the supported anatomical scope of the AI system.
- Written informed consent provided by the participant or legally authorized representative, with age-appropriate assent where applicable.
Exclusion Criteria:
- Age younger than 2 years.
- Injuries involving multiple anatomical regions.
- Head or cervical spine injuries.
- Previous radiographic imaging or medical assessment for the same injury before the index presentation.
- Contraindication to X-ray imaging, including pregnancy.
- Lack of informed consent.
Reduced image quality was not an exclusion criterion. Multiple fractures within the same anatomical injury region were eligible.
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
|---|---|
|
간섭 없음: Standard Radiograph Interpretation Without AI
Plain radiographs are interpreted by the frontline treating physician according to routine clinical practice without access to AI output. All diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. Formal radiology reports are generally available later and are not routinely available before completion of the emergency department encounter. |
|
|
실험적: AI-Assisted Radiograph Interpretation
Plain radiographs are interpreted by the frontline treating physician with access to real-time output from BoneView version 2.3.8 (Gleamer, Paris, France). The AI output is used as diagnostic decision support only; all final diagnostic, imaging, treatment, consultation, and discharge decisions remain the responsibility of the treating physician. |
BoneView version 2.3.8 (Gleamer, Paris, France) analyzes DICOM radiographs and provides real-time visual annotations and classifications for supported musculoskeletal abnormalities. Physicians in the intervention group could view fracture-related findings as well as other supported outputs, including dislocations, joint effusions, and focal bone lesions. At University Hospital Salzburg and Regional Hospital Hallein, BoneView was delivered through the Aidoc aiOS platform (version 3.24.0). At University Hospital Nuremberg, BoneView was integrated directly into the local imaging workflow. BoneView was used as a decision-support tool and did not replace physician interpretation or the subsequent formal radiology report. |
연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Time from Emergency Department Triage to Discharge
기간: During the index emergency department encounter (day 1).
|
Time in minutes from formal emergency department triage to discharge during the index encounter, compared between the AI-assisted and control groups. |
During the index emergency department encounter (day 1).
|
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
|
Missed fractures
기간: At the initial diagnostic assessment during the index emergency department encounter (day 1), with subsequent expert reference-standard adjudication.
|
Proportion of patient encounters with a fracture visible on the index radiographs, according to the expert-adjudicated reference standard, in which the fracture was not identified by the frontline treating physician during the initial diagnostic assessment. The reference standard is established by a radiologist and a senior orthopaedic trauma surgeon, with disagreements resolved by consensus or a third expert. Fractures identified on subsequent imaging but not visible on the original radiographs are not classified as missed radiographically visible fractures. |
At the initial diagnostic assessment during the index emergency department encounter (day 1), with subsequent expert reference-standard adjudication.
|
|
Additional Diagnostic Imaging Requested
기간: During the index emergency department encounter (day 1), following initial radiographic assessment.
|
Proportion of patient encounters in which the treating physician requested additional diagnostic imaging during the index emergency department visit. The frequency of additional imaging requests is compared between the AI-assisted and control groups. |
During the index emergency department encounter (day 1), following initial radiographic assessment.
|
|
Physician Diagnostic Confidence
기간: Measured immediately after the diagnosis
|
The level of confidence reported by physicians in their diagnostic decisions, measured on a Likert scale (1-10). This will compare how confident physicians feel when using AI assistance versus relying solely on their expertise. |
Measured immediately after the diagnosis
|
|
Diagnostic Accuracy of Fracture/Dislocation/Effusion/Bone Lesion Detection
기간: At the time of initial diagnosis, within 2 hours of patient presentation to the orthopedic emergency unit
|
The primary outcome measures the diagnostic accuracy of detecting broken bones/dislocations/effusions/bone lesions using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Diagnostic accuracy will be compared between the AI-assisted diagnostic approach and the standard physician-only approach. The gold standard for comparison will be determined by expert consensus based on independent review by a radiologist and an orthopedic specialist. |
At the time of initial diagnosis, within 2 hours of patient presentation to the orthopedic emergency unit
|
공동 작업자 및 조사자
간행물 및 유용한 링크
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
기본 완료 (실제)
연구 완료 (실제)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
키워드
기타 연구 ID 번호
- KI-FRACTURE_001_2024-11-27
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
IPD 계획 설명
De-identified individual participant data underlying the results reported in the primary publication will be made available to qualified researchers upon reasonable written request.
Shared data may include participant demographics, anatomical injury region, frontline physician diagnosis, AI findings, expert-adjudicated reference-standard findings, diagnostic confidence, additional imaging, and timing data used to derive the primary endpoint.
Data will be shared only for scientifically justified research purposes following review and approval of the proposed project by the study investigators and execution of an appropriate data-sharing agreement. Data sharing will comply with applicable institutional requirements, the General Data Protection Regulation (GDPR), and relevant Austrian and German data-protection legislation.
IPD 공유 기간
Beginning 3 months after publication of the final prespecified FAIR outcome analysis, for 5 years
IPD 공유 액세스 기준
Scientifically sound proposal; investigator review; applicable consent, ethics, institutional and data-protection requirements; appropriate data-sharing agreement
IPD 공유 지원 정보 유형
- 연구_프로토콜
- 수액
약물 및 장치 정보, 연구 문서
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미국 FDA 규제 기기 제품 연구
미국에서 제조되어 미국에서 수출되는 제품
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