MRI of the spine is one of the commonest studies performed in clinical practice usually to study the cause of back pain. The reading of spine MR studies involves identifying the vertebral levels, estimating stenosis of the spinal canal, detecting the reduction in the vertebral height and also identifying spondylolisthesis. Several studies have shown that deep learning can assist in some of these tasks by automating them. In this study, we propose to use a platform-based approach for quick validation of a CNN based multi-modular set of custom Neural Networks that can automatically label the vertebral level and measure the central canal diameter, vertebral height, alignment, and disc height.
The algorithm "Spindle" is a set of neural networks (developed by Synapsica technologies Pvt Ltd) that were trained on 11,321 Spinal MRIs acquired on different field strengths. The independent validation was done on 157 cases of lumbar spine MRI from 1.5 T and 3 T MRI scanners. The results of the algorithm were loaded on CARPL (CARING Analytics PLatform) and the spinal canal diameters at all five lumbar levels were plotted on Dot Plots with an adjustable threshold. Expert validation was done by a neuroradiologist with 9 years' experience. All levels where the network had an output of central canal diameter below 11mm were measured. The accuracy of vertebral labeling and listhesis detection was also verified.
The "Spindle" algorithm shows high accuracy in automatically labeling and quantifying several metrics of spondylosis in this retrospective study. Such automated solutions can enable faster reading and better quantification of imaging findings on MR scans.
Link to complete publication here: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=LSnAkaUAAAAJ&cstart=20&pagesize=80&citation_for_view=LSnAkaUAAAAJ:NMxIlDl6LWMC
