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Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion

Abstract

Purpose

Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.

Methods

Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.

Results

In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.

Conclusions

Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

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Acknowledgements

The authors wish to thank Zakariah Siyaji and Remi Lee for their insights toward this topic.

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Correspondence to Dino Samartzis.

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Rudisill, S.S., Hornung, A.L., Barajas, J.N. et al. Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion. Eur Spine J (2022). https://doi.org/10.1007/s00586-022-07238-3

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  • DOI: https://doi.org/10.1007/s00586-022-07238-3

Keywords

  • Artificial intelligence
  • Machine learning
  • Predictive modeling
  • Anterior cervical discectomy
  • Fusion
  • Cervical spine
  • Adjacent segment
  • Disc
  • Degeneration
  • Outcomes