3D-Printed Colloidal Crystal Hydrogel Crown Fused with Machine Learning-Integrated Resistance Strain Sensor for Pressure Sensing.

Abstract

Advancements in dental restoration technologies have created transformative opportunities for enhancing crown repair through intelligent sensing and adaptive design. While modern materials like ceramics and resins improve aesthetic and functional outcomes, persistent challenges in long-term fit, durability, and dynamic pressure monitoring remain unaddressed. This study introduces a groundbreaking approach that synergizes 3-dimensional (3D)-printed colloidal crystal hydrogel crowns with machine learning-integrated resistance strain sensors. The hydrogel's inverse opal structure ensures robust adhesion to the tooth surface, while embedded strain sensors capture real-time, multidirectional pressure data. Unlike conventional sensing systems, our framework employs machine learning algorithms to dynamically interpret strain patterns, enabling predictive modeling of occlusal forces and adaptive calibration of crown fit. The hydrogel's temperature-responsive properties, combined with sensor stability under oral environmental fluctuations, ensure reliable long-term performance. Machine learning further enhances diagnostic precision by correlating strain-resistance data with clinical parameters, facilitating personalized adjustments to restoration plans. This work pioneers the fusion of intelligent sensing, material innovation, and data-driven analytics in dental care, establishing a foundation for next-generation adaptive and patient-specific restorative solutions.