Using Machine Learning Algorithms to Predict Postoperative Anterior Bone Loss Following Anterior Cervical Disc Replacement.

Abstract

Study design: Machine learning model.

Objectives: This study aimed to develop and validate a machine learning (ML) model to predict moderate-severe anterior bone loss (ABL) following anterior cervical disc replacement (ACDR).

Methods: A retrospective review of patients undergoing ACDR or Hybrid surgery (HS) at a single center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degenerative diseases (CDDD) with more than 2 years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict moderate-severe ABL based on perioperative demographic, clinical, and radiographic parameters. Model performance was evaluated in terms of discrimination and overall performance.

Results: A total of 339 ACDR segments were included (61.65% female, mean age 45.65 ± 8.03 years). During a follow-up period of 45.65 ± 8.03 months, 103 (30.38%) segments developed moderate-severe ABL. The model demonstrated good discrimination and overall performance according to precision (moderate-severe ABL: 0.71 ± 0.07, none-mild ABL: 0.73 ± 0.08), recall (moderate-severe ABL: 0.69 ± 0.08, none-mild ABL: 0.75 ± 0.07), F1-score (moderate-severe ABL: 0.70 ± 0.08, none-mild ABL: 0.74 ± 0.07), and area under the curve (AUC) (0.74 ± 0.10). The most important predictive features were higher height change, higher post-segmental angle, and longer operation time.

Conclusions: Utilizing a ML approach, this study successfully identified risk factors and accurately predicted the development of moderate-severe ABL following ACDR, demonstrating robust discrimination and overall performance. By overcoming the limitations of traditional statistical methods, ML can enhance discovery, clinical decision-making, and intraoperative techniques.