The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies

P. Ojeda; M. Zawaideh; M. Mossa-Basha; D. Haynor



While rapid detection of intracranial hemorrhage (ICH) on computed tomography (CT) is a critical step in assessing patients with acute neurological symptoms in the emergency setting, prioritizing scans for radiologic interpretation by the acuity of imaging findings remains a challenge and can lead to delays in diagnosis at centers with heavy imaging volumes and limited staff resources. Deep learning has shown promise as a technique in aiding physicians in performing this task accurately and expeditiously and may be especially useful in a resource-constrained context. Our group evaluated the performance of a convolutional neural network (CNN) model developed by Aidoc (Tel Aviv, Israel). This model is one of the first artificial intelligence devices to receive FDA clearance for enabling radiologists to triage patients after scan acquisition. The algorithm was tested on 7112 non-contrast head CTs acquired during 2016–2017 from a two, large urban academic and trauma centers. Ground truth labels were assigned to the test data per PACS query and prior reports by expert neuroradiologists. No scans from these two hospitals had been used during the algorithm training process and Aidoc staff were at all times blinded to the ground truth labels. Model output was reviewed by three radiologists and manual error analysis performed on discordant findings. Specificity was 99%, sensitivity was 95%, and overall accuracy was 98%. In summary, we report promising results of a scalable and clinically pragmatic deep learning model tested on a large set of real-world data from high-volume medical centers. This model holds promise for assisting clinicians in the identification and prioritization of exams suspicious for ICH, facilitating both the diagnosis and treatment of an emergent and life-threatening condition.

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