Fractures, Musculoskeletal
Conditions
Brief summary
This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays. By comparing the performance of a deep learning AI model with that of experienced radiologists, we seek to understand how AI can help improve fracture detection accuracy in clinical settings. The study analyzed 600 X-rays from both pediatric and adult patients, focusing on identifying fractures across different body parts, including the foot, ankle, knee, hand, wrist, and more. The findings show that integrating AI can increase radiologists' sensitivity in detecting fractures, potentially improving patient outcomes by reducing the number of missed injuries.
Interventions
DIAGNOSTIC_TESTCarebot AI Bones
The use of a deep learning-based artificial intelligence software, Carebot AI Bones version 1.2.2, designed to aid in the detection of fractures on musculoskeletal radiographs. The AI model analyzes digital X-ray images to identify fractures, highlighting areas of interest with bounding boxes.
Sponsors
Carebot s.r.o.
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
1 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Patients aged 1 year or older. * Musculoskeletal X-rays available in Digital Imaging and Communications in Medicine (DICOM) format. * At least one digital plain radiograph of an appendicular body part, including the foot, ankle, knee, hand, wrist, elbow, shoulder, or pelvis.
Exclusion criteria
* Poor radiographic quality that precludes human interpretation. * Radiographs of the lumbar, thoracic, and cervical spine, or facial/nasal bones. * Radiographs that do not meet the inclusion criteria for appendicular body parts.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays | From March 2023 to May 2023 (Retrospective analysis period) | This outcome measures the sensitivity of the AI model (Carebot AI Bones 1.2.2) in detecting fractures on musculoskeletal X-rays, compared to the sensitivity of radiologists with varying levels of experience. Sensitivity is calculated as the proportion of true positive fracture cases identified by the AI model and radiologists out of all confirmed fracture cases. |
Countries
Czechia
Outcome results
None listed