A Retrospective Multi-reader Study of Diagnostic Performance: Carebot AI Bones 1.2 (Deep Learning Algorithms v1.0), Frýdek-Místek Hospital
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Carebot s.r.o.
- Enrollment
- 600
- Locations
- 1
- Primary Endpoint
- Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 1 Year to — (Child, Adult, Older Adult)
- Sex
- All
- Accepts 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.
Arms & Interventions
Radiographs Analyzed Using AI and Radiologist Review
This cohort consists of 600 radiographs collected from pediatric and adult patients, aged 1 to 99 years, who underwent X-ray imaging for musculoskeletal conditions. The radiographs include various body parts such as the foot, ankle, knee, hand, wrist, elbow, shoulder, and pelvis. Fractures were present in 95 cases, while 453 cases showed no fractures.
Intervention: Carebot AI Bones (Diagnostic Test)
Outcomes
Primary Outcomes
Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays
Time Frame: 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.
Secondary Outcomes
No secondary outcomes reported