Abstract
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.
Highlights
One of the principal reasons for attending the emergency room (ER) is peripheral traumatism
Artificial intelligence algorithms are opening up many new perspectives for radiologists
Diagnoses did not differ between emergency physicians and the radiologist, all of whom identified the same patients as having fractures
Summary
One of the principal reasons for attending the emergency room (ER) is peripheral traumatism. Since 2012, DL has established itself as the cutting-edge method of enhancing performance in medical image analysis, with the use of convolutional neural networks decreasing the classification error rate from about 25% in 2011 to 3.6% in 2015 [3,4]. This success has led to numerous applications in medicine, for identifying and classifying images for diabetic retinopathy [5], and for detecting skin cancer [6] or lesions on mammograms [7]
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