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

NCT06644391 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2026-03-18

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • Carebot s.r.o.

    lead INDUSTRY

Eligibility

Min Age
1 Year
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-03-20
Primary Completion
2024-07-15
Completion
2024-07-15

Countries

  • Czechia

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06644391 on ClinicalTrials.gov