Journal Article

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

Louie, Philip K, An, Howard S, Bridge, Jack J, Rudisill, Samuel S, Hornung, Alexander L, Colman, Matthew W, Mallow, G. Michael, Wilke, Hans-Joachim, Phillips, Frank M, Lopez, Wylie, Tao, Youping, Samartzis, Dino, Sayari, Arash J, Harada, Garrett K, Barajas, J. Nicolás

2022

Overview

PurposeAnterior 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.MethodsRetrospective 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.ResultsIn 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.ConclusionsThrough 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.

• Publisher: Springer Nature B.V
• Subject: Age / Algorithms / Artificial intelligence / Biomechanics / Clinical decision making / Decision making / Degeneration / Intervertebral discs / Machine learning / Magnetic resonance imaging / Nonsteroidal anti-inflammatory drugs / Orthopedics / Osteophytes / Patients / Risk factors / Spine (cervical)