Improving detection of impacted animal bones on lateral neck radiograph using a deep learning artificial intelligence algorithm

ObjectiveWe aimed to develop a deep learning artificial intelligence (AI) algorithm to detect impacted animal bones on lateral neck radiographs and to assess its effectiveness for improving the interpretation of lateral neck radiographs.MethodsLateral neck radiographs were retrospectively collected for patients with animal bone impaction between January 2010 and March 2020. Radiographs were then separated into training, validation, and testing sets. A total of 1733 lateral neck radiographs were used to develop the deep learning algorithm. The testing set was assessed for the stand-alone deep learning AI algorithm and for human readers (radiologists, radiology residents, emergency physicians, ENT physicians) with and without the aid of the AI algorithm. Another radiograph cohort, collected from April 1, 2020, to June 30, 2020, was analyzed to simulate clinical application by comparing the deep learning AI algorithm with radiologists' reports.ResultsIn the testing set, the sensitivity, specificity, and accuracy of the AI model were 96%, 90%, and 93% respectively. Among the human readers, all physicians of different subspecialties achieved a higher accuracy with AI-assisted reading than without. In the simulation set, among the 20 cases positive for animal bones, the AI model accurately identified 3 more cases than the radiologists' reports.ConclusionOur deep learning AI model demonstrated a higher sensitivity for detection of animal bone impaction on lateral neck radiographs without an increased false positive rate. The application of this model in a clinical setting may effectively reduce time to diagnosis, accelerate workflow, and decrease the use of CT.

Automated summary

The study aimed to develop a deep learning AI algorithm for detecting impacted animal bones on lateral neck radiographs and evaluating its effectiveness in improving interpretation. Retrospective lateral neck radiographs were collected and divided into training, validation, and testing sets. The deep learning algorithm achieved a sensitivity of 96%, specificity of 90%, and accuracy of 93% on the testing set. The AI-assisted reading improved accuracy for all physicians and outperformed radiologists' reports in detecting animal bone impaction in a simulated clinical setting.