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.
Study Type
OBSERVATIONAL
Enrollment
19
The intervention is to determine how the radiographer classify emergency radiography.
C.H.U. de Poitiers
Poitiers, France
Evaluate the radiographers' diagnostic performance to issue an advisory opinion to X-ray reading of the appendicular skeleton in emergency department
The primary outcome measure evaluation radiographers' diagnostic will use accurancy, sensibility and specificity.
Time frame: 2 hours
Quantify the proportion of radiographers reaching the goal of 90% accurancy
This is a proportion in % of performance radiographers' diagnosctic treshold
Time frame: 4 months
Quantify and qualify Radiographers diagnostic changes before and after the in house training.
This a proportion in % about the progression of performance radiographers'diagnostic in the formation.
Time frame: 4 months
Evaluate the performance of the association of AI and radiographer after training
Thiis output is the difference between accurancy, sensibility and sensitivity with or without IA.
Time frame: 4 months
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