Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement

Objective: This study aimed to develop and validate a machine learning (ML) model to predict high-grade heterotopic ossification (HO) following Anterior cervical disc replacement (ACDR).

Methods: Retrospective review of prospectively collected data of patients undergoing ACDR or hybrid surgery (HS) at a quaternary referral medical center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degeneration disease with > 2 years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict high grade HO based on perioperative demographic, clinical, and radiographic parameters. Furthermore, model performance was evaluated according to discrimination and overall performance.

Results: In total, 339 ACDR segments were included (61.65% female, mean age 45.65 ± 8.03 years). Over 45.65 ± 8.03 months of follow-up, 48 (14.16%) segments developed high grade HO. The model demonstrated good discrimination and overall performance according to precision (High grade HO: 0.71 ± 0.01, none-low grade HO: 0.85 ± 0.02), recall (High grade HO: 0.68 ± 0.03, none-low grade HO: 0.87 ± 0.01), F1-score (High grade HO: 0.69 ± 0.02, none-low grade HO: 0.86 ± 0.01), and AUC (0.78 ± 0.08), with lower prosthesis‑endplate depth ratio, higher height change, male, and lower postoperative-shell ROM identified as the most important predictive features.

Conclusion: Through an ML approach, the model identified risk factors and predicted development of high grade HO following ACDR with good discrimination and overall performance. By addressing the shortcomings of traditional statistics and adopting a new logical approach, ML techniques can support discovery, clinical decision-making, and intraoperative techniques better.