Deep learning-Based Correlation Analysis of Pelvic and Spinal Sequences for Enhanced Sagittal Spinal Alignment Prediction

Background Pelvic Incidence (PI) plays a crucial role in surgical planning. However, it is insufficient for accurately predicting spinal alignment parameters, including Sacral Slope, Pelvic Tilt, and Lumbar Lordosis. We have devised an AI-based method for predicting sagittal spinal alignments with enhanced precision. Methods We have developed an AI-based system utilizing a Seq2Seq framework to model the spatial correlation between pelvic and spinal key points. This system was trained on a dataset of 337 cases and evaluated using 51 cases obtained from a multi-centre hospital. To address the issue of pelvic rotation, we introduced an Angle Correlation Network. We compared the performance of our AI-based system in predicting spinal alignment against the traditional PI-based method. This comparison was conducted using Mean Absolute Error (MAE) and the Correlation Coefficient (R value) as evaluation metrics. Results We evaluated the performance of our AI-based system for predicting Sacral Slope (SS), Pelvic Tilt (PT), and Lumbar Lordosis (LL) values. The Pearson correlation coefficient of the AI-based method surpassed that of the PI-based method (0.80 vs 0.67 for SS, 0.73 vs 0.52 for PT, and 0.76 vs 0.48 for LL), indicating a more robust linear relationship between AI predictions and actual values. Additionally, the AI-based method exhibited a lower Mean Absolute Error (MAE) compared to the PI-based method for LL (5.52 vs 6.69), signifying enhanced prediction accuracy. Conclusions In this study, we demonstrated the potential of an AI-based approach for predicting sagittal spinal alignments with improved precision compared to the traditional PI-based method. The AI-based system, utilizing a Seq2Seq framework and an Angle Correlation Network, exhibited a stronger linear relationship between predicted and actual values for Sacral Slope, Pelvic Tilt, and Lumbar Lordosis, as well as a reduced Mean Absolute Error for Lumbar Lordosis. These findings support the integration of AI in spinal surgery planning and personalized medicine for sagittal alignment evaluation and management. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Medical Ethics Committee of Chinese PLA General Hospital I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors