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A Deep Metric Learning and Multimodal Gated Fusion Framework for AI‐Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants
0
Zitationen
7
Autoren
2026
Jahr
Abstract
Objective: To provide an automated risk assessment for lingual plate perforation (LPP) and mandibular canal injury (MCI) in dental implants. Also, to reduce interoperator variability in risk classification and eliminate manual measurements during implant planning. Methods: , and ablation studies) were used to evaluate performance. True assessment rate (TAR) was used to assess the model's agreement with human annotators. Grad-CAM visualizations were conducted for qualitative evaluation. Results: = 0.72). Grad-CAM visualizations confirmed the model's ability for feature localization. Conclusion: By integrating imaging and metadata through DML and GFM, the model attempted to reduce interoperator variability in decision-making and enhance risk-classified assessment in the treatment planning process.
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