Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning.

BACKGROUND: Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. OBJECTIVE: A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. MATERIALS AND METHODS: There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. RESULTS: The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. CONCLUSION: A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow.

Measurement of Endotracheal Tube Positioning on Chest X-Ray Using Object Detection.

Patients who are intubated with endotracheal tubes often receive chest x-ray (CXR) imaging to determine whether the tube is correctly positioned. When these CXRs are interpreted by a radiologist, they evaluate whether the tube needs to be repositioned and typically provide a measurement in centimeters between the endotracheal tube tip and carina. In this project, a large dataset of endotracheal tube and carina bounding boxes was annotated on CXRs, and a machine-learning model was trained to generate these boxes on new CXRs and to calculate a distance measurement between the tube and carina. This model was applied to a gold standard annotated dataset, as well as to all prospective data passing through our radiology system for two weeks. Inter-radiologist variability was also measured on a test dataset. The distance measurements for both the gold standard dataset (mean error = 0.70 cm) and prospective dataset (mean error = 0.68 cm) were noninferior to inter-radiologist variability (mean error = 0.70 cm) within an equivalence bound of 0.1 cm. This suggests that this model performs at an accuracy similar to human measurements, and these distance calculations can be used for clinical report auto-population and/or worklist prioritization of severely malpositioned tubes.

Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network.

Aortic dissections and ruptures are life-threatening injuries that must be immediately treated. Our national radiology practice receives dozens of these cases each month, but no automated process is currently available to check for critical pathologies before the images are opened by a radiologist. In this project, we developed a convolutional neural network model trained on aortic dissection and rupture data to assess the likelihood of these pathologies being present in prospective patients. This aortic injury model was used for study prioritization over the course of 4 weeks and model results were compared with clinicians' reports to determine accuracy metrics. The model obtained a sensitivity and specificity of 87.8% and 96.0% for aortic dissection and 100% and 96.0% for aortic rupture. We observed a median reduction of 395 s in the time between study intake and radiologist review for studies that were prioritized by this model. False-positive and false-negative data were also collected for retraining to provide further improvements in subsequent versions of the model. The methodology described here can be applied to a number of modalities and pathologies moving forward.