Nearly equal amounts of positive and negative examinations were included in the analysis. Validation was based on retrospective, blinded data from 47 clinical sites evaluating approximately 8000 examinations. The hardware used for developing and validating the CNN included 8 GPUs, 64 CPUs, 488 GB of RAM, and 128 GB of GPU memory. The CNN is designed to detect linear bony lucency in patterns consistent with fracture (including compression), does not distinguish between acute and chronic fractures, and is limited to the cervical spine (C1–7). We evaluated an FDA-approved CNN developed by Aidoc for cervical spine fracture detection on CT. A proficient algorithm may help identify and triage studies for the radiologist to review more urgently, helping to ensure faster diagnoses. However, we purposefully conducted a retrospective study on cervical spine studies performed before system-wide deployment, as we wanted to compare CNN performance to radiologist performance without the aid of the tool. Aidoc’s CNN currently runs continuously on our hospital system and functions as a triage and notification software for analysis and detection of cervical spine fractures. We establish the presence of fractures based on retrospective clinical diagnosis and compare the CNN performance with that of radiologists. The aim of this study is to evaluate the performance of a convolutional neural network (CNN) developed by Aidoc (for the detection of cervical spine fractures on CT. 21 Morbidity and mortality in patients with cervical spine injury can be reduced through rapid diagnosis and intervention. 17 ⇓ ⇓- 20 Clearing the cervical spine through imaging is therefore a critical first step in the evaluation of patients with trauma, and multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. 15 Cervical spine injury can be associated with high morbidity and mortality, 16 and a delay in diagnosis of an unstable fracture leading to inadequate immobilization may result in a catastrophic decline in neurologic function with devastating consequences. To our knowledge, no studies evaluating AI in detecting cervical spine fractures on CT have been published.Ĭervical spine injury is common with greater than 3 million patients per year being evaluated for cervical spine injury in North America, 14 and greater than 1 million patients with blunt trauma with suspected cervical spine injury per year being evaluated in the United States. 9 In addition, AI has been used to detect calcaneal 10 and thoracic and lumbar vertebral body fractures 11 ⇓- 13 on CT. AI has been used to detect hip, 1- 3 humeral, 4 distal radius, 5 wrist, 6 ⇓- 8 hand, 8 and ankle fractures 8 on radiographs, as well as thoracic and lumbar spine fractures on dual x-ray absorptiometry. ABBREVIATIONS: AI artificial intelligence CNN convolutional neural network NPV negative predictive value PPV positive predictive valueĪ variety of studies have been conducted evaluating the performance of artificial intelligence (AI) to detect fractures.
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