Diagnostic, Emergencies, Radiography
Conditions
Brief summary
This study assesses the performance of radiographers in detecting radiological anomalies of the appendicular skeleton in emergency department. This is a retrospective study comparing the radiographers' diagnostic performance before and after dedicated training, assisted or not by artificial intelligence software. All performances will be evaluated and compared.
Interventions
DIAGNOSTIC_TESTEmergency radiography
The intervention is to determine how the radiographer classify emergency radiography.
Sponsors
Poitiers University Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes
Inclusion criteria
* Working in an emergency radiography department Volunteer to participate in the study
Exclusion criteria
* Planned departure from the establishment within 12 months.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Evaluate the radiographers' diagnostic performance to issue an advisory opinion to X-ray reading of the appendicular skeleton in emergency department | 2 hours | The primary outcome measure evaluation radiographers' diagnostic will use accurancy, sensibility and specificity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Quantify the proportion of radiographers reaching the goal of 90% accurancy | 4 months | This is a proportion in % of performance radiographers' diagnosctic treshold |
| Quantify and qualify Radiographers diagnostic changes before and after the in house training. | 4 months | This a proportion in % about the progression of performance radiographers'diagnostic in the formation. |
| Evaluate the performance of the association of AI and radiographer after training | 4 months | Thiis output is the difference between accurancy, sensibility and sensitivity with or without IA. |
Countries
France
Outcome results
None listed