Ai-Engineered Intelligent Hydrogel System For Controlled Drug Release

Current periodontitis therapies face limitations in biofilm eradication and adaptive treatment modulation, necessitating AI-driven platforms that enable real-time monitoring and dynamic drug adjustment to achieve patient-specific precision control over therapeutic progression. The electromagnetic-responsive dexamethasone-GelMA hydrogel was structurally characterized using SEM, electrochemical analysis, and mechanical testing. A cell culture module applying electromagnetic induction was engineered to evaluate drug release and its cellular effects through cytokine profiling (ELISA), immunofluorescence, flow cytometry, and osteogenic differentiation (ALP staining). In vivo validation in 20 rat models with customized HydroFix Injector combined micro-CT and histopathological multiplexing (H&E, TRAP, Masson, immunohistochemistry) to quantify therapeutic outcomes. PerioNet, a YOLOv8-based model initialized with yolov8s-pose.pt, detects molar bounding boxes and three key anatomical landmarks (cementoenamel junction, alveolar crest, root apex) from 1,605 CT slices (200 epochs, batch=8), enabling geometric quantification of bone resorption to standardize periodontitis diagnosis beyond manual methods. The GelMA-dexamethasone-gallic acid hydrogel achieves anti-inflammatory and osteogenic effects while maintaining mechanical stability, biocompatibility, and intraoral adhesion, enabling targeted periodontitis therapy. Combined with a PerioNet AI model, it enables severity assessment and wireless-controlled adaptive drug release via magnetic-responsive hydrogels. A closed-loop periodontitis therapeutic system demonstrates precision bone regeneration, inflammation suppression, and biocompatibility, validated in a smart intraoral prototype. The study establishes an AI-driven smart hydrogel system for periodontitis that integrates electromagnetic-controlled drug release and image recognition-guided disease staging to achieve closed-loop adaptive therapy.