Magnetic resonance imaging (MRI) algorithm identifies end stage severely degenerated disc as ‘black’, and a moderately degenerate to non-degenerated disc as ‘white’. MRI is based on signal intensity changes that identifies loss of proteoglycans, water, and general radial bulging but lacks association with microscopic features such as fissure, endplate damage, persistent inflammatory catabolism that facilitates proteoglycan loss leading to ultimate collapse of annulus with neo-innervation and vascularization, as an indicator of pain. Thus, we propose a novel machine learning based imaging tool that combines quantifiable microscopic histopathological features with macroscopic signal intensities changes for hybrid assessment of disc degeneration. 100-disc tissue were collected from patients undergoing surgeries and cadaveric controls, age range of 35–75 years. MRI Pfirrmann grades were collected in each case, and each disc specimen were processed to identify the 1) region of interest 2) analytical imaging vector 3) data assimilation, grading and scoring pattern 4) identification of machine learning algorithm 5) predictive learning parameters to form an interface between hardware and software operating system.Background
Methods
