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

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.

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Key information

Conditions

Fractures Fractures, Bone Musculoskeletal Wounds and Injuries

Age range

1 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Nemocnice ve Frýdku-Místku, p.o.

Frýdek-Místek, Moravian-Silesian Region, 73801, Czechia

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

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

Time frame: 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.

Sponsors and collaborators

Lead sponsor

Carebot s.r.o.

Industry

Registry information

Official study title

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

Important dates

Study start: 2023
Primary completion: 2024
Study completion: 2024
First posted: Oct 16, 2024
Registry last updated: Mar 18, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed