• Michael W Dusenberry, Charles K Brown, and Kori L Brewer.
• Brody School of Medicine, East Carolina University, 600 Moye Blvd, Greenville, NC 27834, USA. Electronic address: dusenberrymw@gmail.com.
• Am J Emerg Med. 2017 Feb 1; 35 (2): 260-267.

ObjectivesTo construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall.MethodsAn ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing". Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient.ResultsOn the "cross validation" data, the model attained a sensitivity ("recall") of 100.00%, specificity of 78.95%, PPV ("precision") of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the "test" data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs.ConclusionsANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings.Copyright © 2016 Elsevier Inc. All rights reserved.

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