In Skeletal radiology
OBJECTIVE : Lumbar spine MRI interpretations have high variability reducing utility for surgical planning. This study evaluated a convolutional neural network (CNN) framework that generates automated MRI grading for its ability to predict the level that was surgically decompressed.
MATERIALS AND METHODS : Patients who had single-level decompression were retrospectively evaluated. Sagittal T2 images were processed by a CNN (SpineNet), which provided grading for the following: central canal stenosis, disc narrowing, disc degeneration, spondylolisthesis, upper/lower endplate morphologic changes, and upper/lower marrow changes. The grades were used to calculate an aggregate score. The variables and the aggregate score were analyzed for their ability to predict the surgical level. For each surgical level subgroup, the surgical level aggregate scores were compared with the non-surgical levels.
RESULTS : A total of 141 patients met the inclusion criteria (82 women, 59 men; mean age 64 years; age range 28-89 years). SpineNet did not identify central canal stenosis in 32 patients. Of the remaining 109, 96 (88%) patients had a decompression at the level of greatest stenosis. The higher stenotic grade was present only at the surgical level in 82/96 (85%) patients. The level with the highest aggregate score matched the surgical level in 103/141 (73%) patients and was unique to the surgical level in 91/103 (88%) patients. Overall, the highest aggregate score identified the surgical level in 91/141 (65%) patients. The aggregate MRI score mean was significantly higher for the L3-S1 surgical levels.
CONCLUSION : A previously developed CNN framework accurately predicts the level of microdecompression for degenerative spinal stenosis in most patients.
Roller Brandon L, Boutin Robert D, O’Gara Tadhg J, Knio Ziyad O, Jamaludin Amir, Tan Josh, Lenchik Leon
Automated diagnosis, Low back pain, Lumbar degenerative disc disease, MRI, Machine learning, Microdecompression, Spinal stenosis