Abstract
Many patients who present to their primary care physician for neck pain undergo magnetic resonance imaging (MRI) as part of their diagnostic workup. The physician is then tasked with deciding if the findings of the MRI and workup warrant referral a spine surgery, an intricate task complicated by the high rates of background findings. This results in a high number of non-surgical patients being referred to surgery. Although there are a multitude of reasons for non-surgical patients to still see a subspecialist, deep learning has the potential to help inform physicians of their patients’ surgical candidacy. The preset work describes a proof-of-concept model for evaluating operative candidacy for cervical stenosis only using data from outpatient elective magnetic resonance imaging (MRI) scans. This deep-learning algorithm was trained to automatically segment the areas of both the spinal canal and spinal cord on 100 axial cervical spine MRIs. Once segmented, the model used these areas to generate a biomarker for cervical stenosis, calculated as the minimum difference in cross-sectional area between the spinal canal and the spinal cord within the cervical spine. Following training, the model and its biomarker were tested against a cohort of 147 consecutive patients evaluated in the outpatient setting by a group of board-certified neurosurgeons at our institution for complaints related to their cervical spines. Our automated model determined that the mean minimum difference in cross-sectional area between the spinal canal and spinal cord for our cohort was 35.90±25.00 mm2 for patients who ultimately underwent surgical decompression and 48.55±33.52 mm2 for patients who did not (P=0.005). Using this biomarker, the model distinguished between surgical and non-surgical patients with relatively high accuracy (AUC 0.79). When tested against a novel cohort of outpatient spine surgery clinic patients, the described algorithm determined whether the patient underwent decompression for cervical stenosis using data acquired solely from their cervical spine MRI scans. These findings support a proof-of-concept for our automated deep-learning model and biomarker, which could significantly improve the efficiency of the referral process for patients with neck complaints to a surgical subspecialist.