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

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 followup.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.