Deep Learning-Based Biomechanical Prediction Model for Teaching Anatomy

Abstract

This study aims to develop and validate a biomechanical prediction model leveraging deep learning to enhance anatomy teaching, addressing the pressing need for interactive and accurate educational tools. The research is divided into two critical phases: model development and rigorous validation. We collected comprehensive anatomical data, including MRI and CT scans, along with biomechanical measurements, which were meticulously preprocessed through normalization and augmentation techniques to ensure data quality. A convolutional neural network (CNN) was employed for model development, optimized using the Adam optimizer and mean squared error (MSE) loss function to enhance predictive accuracy. The challenge lies in achieving high fidelity in biomechanical predictions, which are crucial for effective anatomical education. To address this, we implemented cross-validation and tested the model on a separate dataset, evaluating its performance through the coefficient of determination () and root mean squared error (RMSE). The model exhibited exceptional predictive accuracy, with  values of 0.92 and 0.89 for the validation and test sets, respectively. Furthermore, the validated model was seamlessly integrated into an anatomy teaching platform, resulting in a significant improvement in student performance, as evidenced by a marked increase in average scores from 65.3 (pre-test) to 78.9 (post-test). This study underscores the transformative potential of deep learning-based biomechanical prediction models in revolutionizing anatomical education by providing a robust, interactive, and effective learning tool.