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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Moravskoslezský kraj
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Frýdek-Místek, Moravskoslezský kraj, Czechia, 73801
- Nemocnice ve Frýdku-Místku, p.o.
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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 Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
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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.
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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)
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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.
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From March 2023 to May 2023 (Retrospective analysis period)
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Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- CB-BONES-01-FM
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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