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

NCT ID: NCT06644391

Last Updated: 2026-01-14

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 600 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-03-20
• Study Completion Date: 2024-07-15

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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

Carebot AI Bones

Intervention Type: 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.

Interventions

Carebot 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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 1 Year
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Carebot s.r.o.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Nemocnice ve Frýdku-Místku, p.o.

Frýdek-Místek, Moravskoslezský kraj, Czechia

Countries

Czechia

Other Identifiers

CB-BONES-01-FM

Identifier Type: -

Identifier Source: org_study_id