Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning

NCT06644391 | Completed DMC

• 1 other identifier: CB-BONES-01-FM
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

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.

Conditions

Fractures; Musculoskeletal

Outcome Measures

Primary Outcomes (1)

• Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays

  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.

  From March 2023 to May 2023 (Retrospective analysis period)

Study Arms (1)

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.

Diagnostic Test: Carebot AI Bones

Interventions

Carebot AI Bones DIAGNOSTIC_TEST

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.

Radiographs Analyzed Using AI and Radiologist Review

Eligibility Criteria

• Age: 1 Year+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study includes a retrospective cohort of pediatric and adult patients who underwent musculoskeletal radiographs between March 20 and May 8, 2023, in a single-center hospital setting.

You may qualify if:

• 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.

You may not qualify if:

• Poor radiographic quality that precludes human interpretation.
• Radiographs of the lumbar, thoracic, and cervical spine, or facial/nasal bones.

Sponsors & Collaborators

• Carebot s.r.o.: lead

Study Sites (1)

Nemocnice ve Frýdku-Místku, p.o.

Frýdek-Místek, Moravskoslezský kraj, 73801, Czechia

Location

MeSH Terms

Conditions

Fractures, Bone

Condition Hierarchy (Ancestors)

Wounds and Injuries

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 14, 2024
• First Posted: October 16, 2024
• Study Start: March 20, 2023
• Primary Completion: July 15, 2024
• Study Completion: July 15, 2024
• Last Updated: March 18, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

CZ (1)