Highly-sensitive ultra-thin dental patches assisted with artificial-intelligence recognition for mapping hidden periodontitis lesions

Abstract

Accurate detection and early diagnosis of periodontitis can be potentially achieved by detecting volatile sulfur compounds (VSCs) produced by pathogenic bacteria beneath the gingiva. However, current methods for detecting VSCs face limitations due to their reliance on expensive and bulky instruments, long testing times, or the requirement of customized mouthguards. Herein, we propose a simple yet effective strategy to improve the sensitivity of ZnO quantum dots (QDs) to VSCs by modulating surface defects of ZnO QDs and report the development of the as-prepared highly sensitive fluorescent ZnO-PDMS wearable dental patch sensor. With the assistance of artificial intelligence (AI) recognizing the fluorescence quenching areas, the ZnO-PDMS dental patches are demonstrated to accurately detect the local release of VSCs (e.g., H₂S) in vivo within 15 min, thereby enabling convenient mapping and screening for hidden periodontitis lesions. Due to its facile preparation, ease of use, and rapid and accurate mapping ability, the ZnO-PDMS dental patches demonstrate significant potential in routine screening, treatment assisting, and the monitoring of periodontitis.