Artificial neural networks for screening patients needing emergency cranial computed tomography scans in emergency...

We evaluated the potential for a neural network to screen candidates for emergency cranial computed tomography (CT) scans in an emergency department setting.

Data were collected from 1625 patients undergoing emergency cranial CT scanning in two different emergency departments (EDs). Singular value decomposition (SVD) was used to remap input data for network training. Data were randomly divided into six subsets, and one was reserved as a test set to analyze network performance. Five networks were then trained on data from the five remaining sets using fivefold cross-validation. Each trained network was allowed an independent vote on need for CT scanning in each case from the test set. The majority vote was used as the final prediction. A similar analysis was done on data from each individual ED. Results are compared with prior statistical studies of the same data.

The network performed well when predicting clinical variable patterns that consistently produced negative CT scans and on patterns that were ambiguous in terms of the CT scan results. It performed poorly, however, on patterns that consistently predicted positive scans. This last finding appears to have resulted from inadequate training material. The two populations from which data were taken were shown to be distinct, but a network trained on the combined data performed as well as the networks from the individual EDs in predicting patients requiring CT scanning. Variables with the greatest contribution to the networks' prediction were consistent with those in prior statistical studies.

Although preliminary in nature, neural networks show promise as a screening device for selecting patients for emergent cranial CT scanning.