![]() IVC filters are protective against pulmonary embolus in the acute setting, but in some cases have demonstrated an increased risk of recurrent DVT or caval thrombosis in the long term. Inferior vena cava (IVC) filters are used to prevent development of potentially life-threatening pulmonary embolism in patients with deep venous thrombosis (DVT), most often when patients have an absolute or relative contraindication to treatment with anticoagulation. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm. Conclusionįully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7–98.1%) and a specificity of 98.9% (95% CI 97.4–99.5%). ![]() The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. ![]() MethodsĪ primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.
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