Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning

Abstract

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.

Applications of Deep Learning for Drug Discovery Systems with BigData

“…Among these, research on the classification and clustering of biological images using machine learning has been the most-studied area. Applied research, such as lesion detection from medical images, has also actively been conducted [29][30][31]. Since image classification is an important research step in the analysis of biological image data, a method for classifying biological image data with high accuracy and high throughput is desired.…”