NCT Number: NCT06754137
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.
Interested in participating?
Request InfoAll sexes
Interventional
Not applicable
Landesklinik Hallein, Salzburger Landeskliniken, Hallein, Austria
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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.
Physicians interpret X-ray images using their standard diagnostic practices without any assistance from AI. This represents the traditional approach to diagnosing fractures.
Time frame: 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.
Time frame: During the patient's emergency department visit, typically within 4 hours of presentation.
The time required to establish a diagnosis, measured from the moment the patient undergoes X-ray imaging to the time the final diagnosis is recorded. This will compare the efficiency of the AI-assisted diagnostic workflow with the standard physician-only workflow.
Time frame: 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.
Time frame: Calculated at the end of the study for all enrolled participants, approximately 6 months from study initiation.
A cost analysis evaluating the total costs associated with the diagnostic process in each group, including personnel time, resource utilization, and any additional procedures required. This will determine whether AI-assisted diagnostics reduce overall healthcare costs compared to standard practices.
Contact information is provided by the study sponsor or research team.
Salzburger Landeskliniken
Other
Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT03506958
Dislocations, Fractures, Bone
Lemmer, Provincie Friesland, Netherlands
View Trial DetailsNCT04389749
Fractures, Bone, Wounds and Injuries
Cincinnati, Ohio, United States
View Trial DetailsNCT06113211
Fractures, Bone, Opioid Use
Detroit, Michigan, United States
View Trial DetailsNCT06793644
Accidental Injuries, Fractures, Bone
Lebanon, New Hampshire, United States
View Trial Details