CT Cervical Spine Fracture Detection Using a Convolutional Neural Network

Abstract

BACKGROUND AND PURPOSE: Multidetector CT has emerged as the standard of care imaging technique to evaluate cervical spine trauma. Our aim was to evaluate the performance of a convolutional neural network in the detection of cervical spine fractures on CT. MATERIALS AND METHODS:We evaluated C-spine, an FDA-approved convolutional neural network developed by Aidoc to detect cervical spine fractures on CT. A total of 665 examinations were included in our analysis. Ground truth was established by retrospective visualization of a fracture on CT by using all available CT, MR imaging, and convolutional neural network output information. The OE coefficients, sensitivity, specificity, and positive and negative predictive values were calculated with 95% CIs comparing diagnostic accuracy and agreement of the convolutional neural network and radiologist ratings, respectively, compared with ground truth. RESULTS:Convolutional neural network accuracy in cervical spine fracture detection was 92% (95% CI, 90%-94%), with 76% (95% CI, 68%-83%) sensitivity and 97% (95% CI, 95%-98%) specificity. The radiologist accuracy was 95% (95% CI, 94%-97%), with 93% (95% CI, 88%-97%) sensitivity and 96% (95% CI, 94%-98%) specificity. Fractures missed by the convolutional neural network and by radiologists were similar by level and location and included fractured anterior osteophytes, transverse processes, and spinous processes, as well as lower cervical spine fractures that are often obscured by CT beam attenuation. CONCLUSIONS:The convolutional neural network holds promise at both worklist prioritization and assisting radiologists in cervical spine fracture detection on CT. Understanding the strengths and weaknesses of the convolutional neural network is essential before its successful incorporation into clinical practice. Further refinements in sensitivity will improve convolutional neural network diagnostic utility.

Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations

“…2, the amount CTs for training, validation and testing are plotted against the accuracy, with increasing accuracy from left to right. The study with the most CTs reported an average accuracy of 92% [14]. The highest accuracy of 97% was reported in a study [17] with only 612 CTs.…”

“…All studies clearly stated their study aim, model used and how performance was measured. The input feature was not clearly specified in three studies [11,13,14]. These studies did not mention what the inclusion and exclusion criteria were.…”

“…All seventeen studies used a CNN to detect and /or classify fractures on CT scans [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Eight studies addressed detection of rib fractures [13,17,19,20,22,[25][26][27], three studies the performance for detection [12,21] and classification [18] of pelvic fractures, four for detection of spine fractures [14,16,23,28], one for detection and classification of femur fractures [24] and one of calcaneal fractures [15]. Fourteen studies used two output classes (fracture yes/no).…”

“…One study on spine fractures used three output classes: completely displaced, incompletely displaced and compression fracture [14]. In addition, two studies used fresh, healing and old fracture as output classes [25,26].…”

Systematic analysis of the test design and performance of AI/ML-based medical devices approved for triage/detection/diagnosis in the USA and Japan

“…Despite the extensive testing procedures that were performed before approval, there were instances where the clinical performance of approved devices did not measure up to expectations 74 – 76 . This was the case with BriefCase for CSF Triage (Aidoc Medical Ltd.) and Health VCF (Zebra Medical Vision Ltd.).…”