CT Cervical Spine Fracture Detection Using a Convolutional Neural Network

Location of false-negative and false-positive fractures for the radiologists and the CNN. Each location instance is marked by a red dot. False-negative fractures by radiologists (A) and the CNN (B) were similar in site and distribution. Although both the radiologists and CNN missed fractures more commonly along the lower cervical spine, errors were more numerous for the CNN. False-positive fracture sites noted by radiologists (C) and the CNN (D) can also be compared side-by-side. Numerous findings can mimic fractures on CT, most commonly degenerative changes or nutrient foramina. These fracture mimics were misinterpreted by both radiologists and the CNN. False-positive fractures along the anterior corners of vertebral bodies were slightly more commonly noted by radiologists and false-positive fractures along the facets and transverse processes were more commonly identified by the CNN.

Fracture-positive, radiologist false-negative, CNN false-negative case example. Axial (A), sagittal (B), and coronal (C) cervical spine CT images, and sagittal fat-saturated T2-weighted cervical spine MR image (D) demonstrate a minimally displaced C4 spinous process fracture. Red arrows demarcate fracture lines, the blue arrow demarcates prevertebral edema, and the orange arrow demarcates interspinous and supraspinous ligamentous injury. This case example illustrates a subtle fracture missed by both the radiologist and CNN that was identified in retrospect with the help of MR imaging because of the presence of secondary signs, such a prevertebral edema and ligamentous injury.

Fracture-positive, radiologist true-positive, CNN false-negative case example. Axial (A) and sagittal (B) cervical spine CT images, and sagittal fat-saturated T2-weighted cervical spine MR image (C) demonstrate a C6–7 fracture-dislocation with cord compression. Red arrows demarcate fracture-dislocation, the blue arrow demarcates prevertebral edema, and the orange arrow demarcates cord compression. This case example illustrates an important drawback of the CNN to overlook areas of gross bony translation, as it was only designed to detect linear bony lucency in patterns consistent with fractures.

Fracture-positive, radiologist true-positive, CNN false-negative case example. Axial (A) and sagittal (B) cervical spine CT images, and sagittal fat-saturated T2-weighted cervical spine MR image (C) demonstrate multiple fractures involving the C7 and T1 vertebral bodies and C6 spinous process with epidural hematoma and associated cord compression. Red arrows demarcate fracture lines and the blue arrow demarcates epidural hematoma. This case example illustrates important drawbacks of the algorithm to miss fractures characterized more by distraction rather than linear bony lucency, fractures involving the distal aspects of the spinous processes that may be mistaken for nuchal ligament calcification or ossification, and fractures located in the lower cervical spine where fine bony detail becomes poor from CT beam attenuation.

Fracture-positive, radiologist false negative, CNN true positive case example. Axial (A) and sagittal (B) cervical spine CT images and sagittal (C) STIR cervical spine MR image demonstrate a minimally displaced right C7 superior articulating facet fracture. Red and blue arrows demarcate fracture lines. This case example illustrates the strength of the algorithm to detect subtle fractures when they conform to linear bony lucencies.

Fracture-negative, radiologist false positive, CNN false positive case example. Sagittal (A) cervical spine CT image and sagittal (B) STIR and sagittal (C) T1-weighted cervical spine MR images demonstrate a small depression along the anterosuperior margin of the C6 vertebral body (red arrow) without associated bone marrow edema, prevertebral edema, or disc space widening (blue circles). This case example illustrates how both the radiologist and CNN algorithm are capable of ignoring the absence of secondary signs, such as prevertebral edema and disc space widening when incorrectly identifying a fracture.

Fracture-negative, radiologist false positive, CNN true negative case example. Sagittal (A) cervical spine CT image and sagittal (B) STIR cervical spine MR image demonstrate a nutrient foramen/trabecular variant within the dens (red arrow) without associated bone marrow edema (blue circle). This case example illustrates the ability of the AI algorithm to exclude those linear bony lucencies that contain sclerotic margins inconsistent with fracture.

Fracture-negative, radiologist true negative, CNN false positive case example. Axial (A) and sagittal (B) cervical spine CT images demonstrate congenital thinning and incomplete fusion of the left C1 lamina. Red arrows demarcate congenital thinning and incomplete fusion. This case example illustrates a limitation of the AI algorithm to mistake common congenital anomalies for fractures if the image contains linear bony lucency extending into the cortex.