NCT06644391

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

A Retrospective Multi-reader Study of Diagnostic Performance: Carebot AI Bones 1.2 (Deep Learning Algorithms v1.0), Frýdek-Místek Hospital

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 600 (actual)
• Sponsor: Carebot s.r.o. · Industry
• Sex: All
• Age: 1 Year
• Healthy volunteers: Not accepted

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

Type	Name	Description
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.

Timeline

• Start date: 2023-03-20
• Primary completion: 2024-07-15
• Completion: 2024-07-15
• First posted: 2024-10-16
• Last updated: 2026-03-18

Locations

1 site across 1 country: Czechia