A Deep Learning Model for the Accurate and Reliable Classification of Disc Degeneration Based on MRI Data.

Investigative Radiology
Frank NiemeyerHans-Joachim Wilke

Abstract

Although magnetic resonance imaging-based formalized grading schemes for intervertebral disc degeneration offer improved reproducibility compared with purely subjective ratings, their intrarater and interrater reliability are not nearly good enough to be able to detect small to medium effects in clinical longitudinal studies. The aim of this study thus was to develop a method that enables automatic and therefore reproducible and reliable evaluation of disc degeneration based on conventional clinical image data and Pfirrmann's grading scheme. We propose a classifier based on a deep convolutional neural network that we trained on a large, manually evaluated data set of 1599 patients (7948 intervertebral discs). To improve upon the status quo, we focused on the quality of the training data and performed extensive hyperparameter optimization. We assessed the potential benefits of optimizing loss functions beyond common cross-entropy loss, such as soft kappa loss, ordinal cross-entropy loss, or regression losses. We furthermore experimented with ways to mitigate class imbalance by pooling classes or using class-weighted loss functions. During model development and hyperparameter optimization, we used a fixed 90%/10% training/validat...Continue Reading

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Citations

Apr 6, 2021·Investigative Radiology·Val M Runge, Johannes T Heverhagen
Sep 11, 2021·Reviews in the Neurosciences·Brian FianiClaudia Covarrubias

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