Deep Learning-Based Predictive Analytics for Intraoperative Spinal Stability and Post-Surgical Biomechanical Load Distribution

Abstract

Background: Recent advancements in deep learning have revolutionized predictive analytics in medicine. In spinal surgery, innovative algorithms address intraoperative stability and post-surgical biomechanical challenges significantly effectively. Objective: To evaluate deep learning-based predictive analytics for enhancing intraoperative spinal stability and post-surgical biomechanical load distribution, aiming to quantify surgical outcome improvements and optimize implant performance in clinical practice effectively. Methods: This prospective study was conducted at the Department of Orthopaedic Surgery, Rajshahi Medical College from June 2021 to December 2023. Sixty-eight patients undergoing spinal surgery were evaluated. Preoperative imaging, intraoperative sensor data, and finite element analysis were integrated into a convolutional neural network model. Data were analyzed at 3, 6, and 12-months follow-up. Rigorous statistical validation ensured model reliability. Results: The deep learning model demonstrated significant predictive accuracy, with a sensitivity of 92% and specificity of 88% in identifying intraoperative instability. Quantitative analysis revealed a 35% reduction in postoperative complications and a 40% improvement in load distribution efficiency. Calculation results indicated that mean spinal alignment improved from 68.2° preoperatively to 75.6° postoperatively, representing a 10.5% enhancement. Furthermore, 85% of patients exhibited favorable outcomes at 12-month follow-up, confirming the model’s efficacy. Additional calculations confirmed the model's robustness, showing an overall accuracy of 90% and an 87% positive predictive value, emphasizing its clinical applicability. Statistical significance achieved at p<0.01 across all metrics. Conclusion: Deep learning-based predictive analytics substantially improve intraoperative decision-making and post-surgical outcomes by enhancing spinal stability and load distribution. This study supports clinical integration of the model for optimized patient care.