Imaging of head trauma

Labeling Noncontrast Head CT Reports for Common Findings Using Natural Language Processing

BACKGROUND AND PURPOSE

Prioritizing reading of noncontrast head CT examinations through an automated triage system may improve time to care for patients with acute neuroradiologic findings. We present a natural language-processing approach for labeling findings in noncontrast head CT reports, which permits creation of a large, labeled dataset of head CT images for development of emergent-finding detection and reading-prioritization algorithms.

MATERIALS AND METHODS

In this retrospective study, 1002 clinical radiology reports from noncontrast head CTs collected between 2008 and 2013 were manually labeled across 12 common neuroradiologic finding categories. Each report was then encoded using an n-gram model of unigrams, bigrams, and trigrams. A logistic regression model was then trained to label each report for every common finding. Models were trained and assessed using a combination of L2 regularization and 5-fold cross-validation.

RESULTS

Model performance was strongest for the fracture, hemorrhage, herniation, mass effect, pneumocephalus, postoperative status, and volume loss models in which the area under the receiver operating characteristic curve exceeded 0.95. Performance was relatively weaker for the edema, hydrocephalus, infarct, tumor, and white-matter disease models (area under the receiver operating characteristic curve > 0.85). Analysis of coefficients revealed finding-specific words among the top coefficients in each model. Class output probabilities were found to be a useful indicator of predictive error on individual report examples in higher-performing models.

CONCLUSIONS

Combining logistic regression with n-gram encoding is a robust approach to labeling common findings in noncontrast head CT reports.

Sharp penetrating wounds: spectrum of imaging findings and legal aspects in the emergency setting

The main cause of severe civilian trauma is not the same all over the world; while in Europe the majority of cases are due to blunt traumatic injury, in the United States, penetrating gunshot wounds are the most common. Penetrating wounds can be classified into two different entities: gunshot wounds, or more technically ballistic traumas, and sharp penetrating traumas, also identifiable with non-ballistic traumas. Sharp penetrating injuries are mainly caused by sharp pointed objects such as spears, nails, daggers, knives, and arrows. The type of injuries caused by sharp pointed objects depends on the nature and shape of the weapon, the amount of energy in the weapon or implement when it strikes the body, whether it is inflicted upon a moving or a still body, and the nature of the tissue injured. In the assessment of hemodynamically stable patients with sharp penetrating wounds, the main imaging procedure is Multidetector Computed Tomography (MDCT), especially used in complicated cases of penetrating injuries with an important impact on the final therapeutic choice. The diagnostic approach has been changed by MDCT due to its technical improvements, in particular, faster data acquiring and upgraded image reconstructions.