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

Completed

• Conditions: Fractures; Musculoskeletal

• Registration Number: NCT06644391
• Lead Sponsor: Carebot s.r.o.

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-03-20
• Primary Completion: 2024-07-15
• Study Completion: 2024-07-15
• Status Verified: 2024-10
• Study First Submitted: 2024-10-14
• Study First Submitted QC: 2024-10-14
• Last Update Submitted: 2024-10-15
• Study First Posted: 2024-10-16
• Last Update Posted: 2024-10-17

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 600

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Sensitivity of AI Model Compared to Radiologists in Fracture Detection on Musculoskeletal X-rays	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.

Trial Locations

Locations (1)

Nemocnice ve Frýdku-Místku, p.o.; 🇨🇿 Frýdek-Místek, Moravskoslezský, Czechia