• CheungJason Pui YinJPYDepartment of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, 5/F Professorial Block, Pokfulam, Hong Kong. cheungjp@hku.hk., Xihe Kuang, Marcus Kin Long Lai, Kenneth Man-Chee Cheung, Jaro Karppinen, Dino Samartzis, Honghan Wu, Fengdong Zhao, Zhaomin Zheng, and Teng Zhang.
• Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, 5/F Professorial Block, Pokfulam, Hong Kong. cheungjp@hku.hk.
• Eur Spine J. 2022 Aug 1; 31 (8): 196019681960-1968.

BackgroundLumbar 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.PurposeWe aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs.Materials And MethodsWe 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.ResultsOur 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%).ConclusionThis 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.© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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