Assessing AI-Supported Fracture Detection in Emergency Care Units

NCT ID: NCT06754137

Last Updated: 2026-01-22

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 4800 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-03-31
• Study Completion Date: 2026-04-30

Brief Summary

Brief Summary The purpose of this study is to determine if artificial intelligence (AI) can assist doctors in detecting broken bones, effusions, dislocations and bone lesions more quickly and accurately in an emergency room setting. The study will also evaluate whether AI can save time and reduce costs in healthcare.

The main questions to be addressed are:

• Does AI improve the accuracy of detecting broken bones/dislocations/effusions/bone lesions?
• Can AI expedite the process of diagnosing broken bones/dislocations/effusions/bone lesions?
• Does AI reduce healthcare costs by enhancing efficiency?

To investigate these questions, two groups of patients will be compared. One group will follow the traditional diagnostic approach, while the other group will utilize AI to assist in diagnosing X-rays.

Participants in the study will:

Undergo standard X-ray imaging of injured arms or legs, as part of routine care.

Have X-rays reviewed by doctors with or without AI support, depending on the assigned group.

The study will include patients of all ages presenting to the emergency room with an isolated injury or joint complaints. No additional tests or treatments beyond standard care will be involved.

Detailed Description

This clinical trial aims to evaluate the cost-efficiency and workflow impact of AI-assisted fracture detection in an orthopedic emergency care unit. The study is designed as a prospective, randomized, controlled trial to assess whether integrating AI technology can improve diagnostic accuracy, streamline workflow, and reduce healthcare costs compared to the traditional diagnostic approach.

Study Objectives

Primary Objectives:

The primary objective of the SMART Fracture Trial is to assess the impact of AI-assisted X-ray interpretation on physician decision-making and clinical workflows. The study will therefore provide deeper insights into AI's potential benefits and limitations beyond theoretical performance metrics.

Secondary Objectives:

While the primary focus of the SMART Fracture Trial is on AI's clinical integration, the study will also comprehensively assess diagnostic accuracy and classification performance - key factors that influence real-world implementation. By analyzing these secondary objectives, the study will provide deeper insights into AI's theoretical performance metrics.

Study Design

This is a prospective, randomized, controlled trial conducted as an international multi-center study. It includes two parallel arms:

Control Group: Standard diagnostic procedures without AI assistance. Intervention Group: AI-based diagnostic tools assist in interpreting radiological images.

Both groups will follow the same diagnostic imaging protocol, including standard X-ray imaging in two planes. The AI software, pre-validated for fracture detection, will be integrated into the hospital's Picture Archiving and Communication System (PACS).

Intervention Details

The AI fracture detection systems (Aidoc, Gleamer) are designed to identify fracture patterns, bone lesions, effusion and dislocations on X-rays and highlight areas of potential concern for physician review. The software operates in real time, providing marked-up images to physicians. The AI output serves as a diagnostic aid, with final diagnoses made by the attending physician.

Population and Sampling

Population: Patients of all ages presenting to the emergency care unit with isolated extremity injuries or isolated joint complaints.

Sample Size: Approximately 4,800 participants (2400 per group) to ensure sufficient statistical power for primary outcomes.

Randomization: Participants will be randomly assigned to the control or intervention group using a 1:1 allocation ratio.

Outcome Measures

Primary Outcome Measures:

Diagnostic accuracy: Sensitivity, specificity, and AUC of AI-assisted vs. traditional diagnosis.

Time to diagnosis: Total time from patient triage to final diagnosis.

Secondary Outcome Measures:

Cost analysis: A detailed cost comparison of the diagnostic process in both groups.

Diagnostic confidence: Assessed using a Likert scale (1-10) completed by physicians after reviewing each case.

Study Procedures

Baseline Data Collection: Demographics, clinical history, and presenting symptoms will be recorded at enrollment. Standard radiological imaging will be conducted for all participants.

AI Integration (Intervention Group): Radiological images will be processed by AI software, providing annotated images to physicians. AI-assisted diagnostic workflows will be compared to standard workflows.

Outcome Assessment: All diagnoses will be independently reviewed by a panel of experts, including an experienced radiologist and orthopedic surgeon, to establish a reference standard for comparison.

Ethical Considerations

The study adheres to the principles of the Declaration of Helsinki and has received approval from the local ethics committee. Written informed consent will be obtained from all participants before enrollment. Data will be pseudonymized to maintain confidentiality.

Expected Impact

This study aims to provide robust evidence regarding the effectiveness of AI in improving diagnostic workflows in emergency care settings. Findings may inform the future integration of AI tools into clinical practice, improving patient outcomes and optimizing resource utilization in high-volume emergency care environments.

Conditions

Fractures, Bone; Effusion Joint; Bone Lesion; Dislocation

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Diagnostics without AI

Standard diagnostic approach where physicians interpret X-ray images without AI assistance.

Group Type: ACTIVE_COMPARATOR

Standard Physician-Interpreted Fracture Detection

Intervention Type: DIAGNOSTIC_TEST

Physicians interpret X-ray images using their standard diagnostic practices without any assistance from AI. This represents the traditional approach to diagnosing fractures.

Diagnostics with AI

Diagnostic approach where physicians are supported by an AI system (Aidoc or Gleamer BoneView) for fracture detection on X-ray images.

Group Type: EXPERIMENTAL

AI-Assisted Fracture Detection System

Intervention Type: DIAGNOSTIC_TEST

The intervention involves the use of an AI-assisted fracture detection system (Aidoc or Gleamer BoneView), which is integrated into the hospital's Picture Archiving and Communication System (PACS). These AI tools analyze X-ray images in real time, highlighting potential fracture sites for physician review. The AI output serves as an additional aid, while the final diagnosis remains the responsibility of the physician.

Interventions

AI-Assisted Fracture Detection System

The intervention involves the use of an AI-assisted fracture detection system (Aidoc or Gleamer BoneView), which is integrated into the hospital's Picture Archiving and Communication System (PACS). These AI tools analyze X-ray images in real time, highlighting potential fracture sites for physician review. The AI output serves as an additional aid, while the final diagnosis remains the responsibility of the physician.

Intervention Type: DIAGNOSTIC_TEST

Standard Physician-Interpreted Fracture Detection

Physicians interpret X-ray images using their standard diagnostic practices without any assistance from AI. This represents the traditional approach to diagnosing fractures.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Presenting to the emergency department with an isolated injury or joint complaint
• Patients able and willing to provide informed consent.

Exclusion Criteria

• Patients with injuries or complaints involving multiple body regions
• Patients with prior imaging of the affected extremity or region within the past 6 months
• Contraindications to X-ray imaging (e.g., pregnancy or severe instability)
• Patients with other ongoing studies that may interfere with this study
• Patients unable to provide consent due to cognitive impairment or language barriers without an available representative.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Klinikum Nürnberg

OTHER

Sponsor Role: collaborator

Salzburger Landeskliniken

OTHER

Sponsor Role: lead

Responsible Party

Martin Breitwieser

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Landesklinik Hallein, Salzburger Landeskliniken

Hallein, , Austria

Site Status: NOT_YET_RECRUITING

University Hospital Salzburg, Salzburger Landeskliniken

Salzburg, , Austria

Site Status: RECRUITING

University Hosptial Nuremberg, Klinikum Nürnberg

Nuremberg, , Germany

Site Status: RECRUITING

Countries

Austria; Germany

Central Contacts

Martin Breitwieser, MD, MBA, BSc

Role: CONTACT

m.breitwieser@salk.at

+43 5 7255 ext. 54705

Facility Contacts

Sebastian Filipp, MD

Role: primary

s.filipp@salk.at

+43 57 25544 55354

Martin Breitwieser, MD, MBA, BSc

Role: primary

m.breitwieser@salk.at

+43 57 2550 54705

Thomas Reuter, MD

Role: primary

thomas.reuter@klinikum-nuernberg.de

+49 911 3982600

References

Breitwieser M, Zirknitzer S, Poslusny K, Freude T, Scholsching J, Bodenschatz K, Wagner A, Hergan K, Schaffert M, Metzger R, Marko P. AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model. Diagnostics (Basel). 2025 Aug 21;15(16):2117. doi: 10.3390/diagnostics15162117.

Reference Type: DERIVED

PMID: 40870969 (View on PubMed)

Other Identifiers

KI-FRACTURE_001_2024-11-27

Identifier Type: -

Identifier Source: org_study_id