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

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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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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, USA

    Samuel S. Rudisill, Alexander L. Hornung, J. Nicolás Barajas, Jack J. Bridge, G. Michael Mallow, Wylie Lopez, Arash J. Sayari, Garrett K. Harada, Matthew W. Colman, Frank M. Phillips, Howard S. An & Dino Samartzis

  2. International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA

    Samuel S. Rudisill, Alexander L. Hornung, J. Nicolás Barajas, G. Michael Mallow, Wylie Lopez, Arash J. Sayari, Garrett K. Harada, Matthew W. Colman, Frank M. Phillips, Howard S. An & Dino Samartzis

  3. Department of Data Science and Analytics, University of Missouri, Colombia, MO, USA

    Jack J. Bridge

  4. Virginia Mason Medical Center, Neuroscience Institute, Seattle, WA, USA

    Philip K. Louie

  5. Institute of Orthopaedic Research and Biomechanics, Ulm University Medical Centre, Ulm, Germany

    Youping Tao & Hans-Joachim Wilke

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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 31, 2104–2114 (2022). https://doi.org/10.1007/s00586-022-07238-3

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

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