Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation

Background Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. Purpose We aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs. Materials and methods We utilized our dataset with MRIs acquired from 1,343 individual participants (taken at the baseline and the 5-year follow-up timepoint), and progression assessments (the Schneiderman score, disc bulging, and Pfirrmann grading) that were labelled by spine specialists with over ten years clinical experience. Our new pipeline was realized by integrating the MRI-SegFlow and the Visual Geometry Group-Medium (VGG-M) for automated disc region detection and LDD progression prediction correspondingly. The LDD progression was quantified by comparing the Schneiderman score, disc bulging and Pfirrmann grading at the baseline and at follow-up. A fivefold cross-validation was conducted to assess the predictive performance of the new pipeline. Results Our pipeline achieved very good performances on the LDD progression prediction, with high progression prediction accuracy of the Schneiderman score (Accuracy: 90.2 +/- 0.9%), disc bulging (Accuracy: 90.4% +/- 1.1%), and Pfirrmann grading (Accuracy: 89.9% +/- 2.1%). Conclusion This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts.

Automated summary

The study aimed to establish a deep learning-based pipeline for predicting the progression of lumbar disc degeneration (LDD), which achieved high prediction accuracy without human interference.