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Ai-Assisted Invisible Orthodontics For Remote Diagnosis And Malocclusions Monitoring

作者
Hao Feng,Xibo Pei,Jian Wang
出处
期刊:International Dental Journal [Elsevier BV]
卷期号:75: 105009-105009
标识
DOI:10.1016/j.identj.2025.105009
摘要

Aim or purpose: Develop an AI-driven integrated sensing system for orthodontic monitoring and malocclusion diagnosis using visual sensors and piezoelectric nanogenerators for real-time monitoring. Materials and methods: High-performance piezoelectric sensors were embedded into the aligner's occlusal surface via flexible printed circuits, creating a fully integrated, flexible, and transparent orthodontic device. An orthodontic attachment, synthesized through supramolecular interactions, combines mechanoluminescence and biosensing by incorporating strontium aluminate crystals that emit fluorescence under orthodontic forces. The biosensor precisely measures lactate levels to detect bacterial infections. A piezoelectric sensor matrix on the occlusal surface continuously monitors force and transmits data via Bluetooth. Over 1,400 datasets were analyzed using machine learning algorithms to identify and track harmful oral habits such as lip biting, thumb sucking, and bruxism. Results: The integration of mechanoluminescence and biosensing enabled bimodal, interference-free visual detection of orthodontic forces and bacterial infections. Orthodontic forces activated fluorescence in strontium aluminate crystals, while biosensors accurately measured lactate levels, demonstrating multifunctional capabilities. Embedded piezoelectric sensors showed high sensitivity in detecting bite forces across multiple sites and maintained continuous pressure monitoring. The AI system, enhanced with machine learning, achieved a 95% diagnostic accuracy for various malocclusion types and effectively identified harmful oral habits, validating the system’s reliability and efficiency in orthodontic monitoring. Conclusions: The AI-driven integrated sensing system represents a significant advancement in orthodontic monitoring and diagnosis. By combining mechanoluminescent and biosensing materials with high-performance piezoelectric sensors and machine learning algorithms, the system provides precise, real-time data on orthodontic forces and oral health indicators.
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