Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk

Reliable risk assessment for implant-supported restorations in the esthetic zone is critical yet challenging due to complex anatomical variations and the inherent subjectivity of traditional clinical assessments. To address these limitations, we developed a multi-functional artificial intelligence (AI) system designed to automate the assessment of radiographic surrogate markers of implant esthetic risk, specifically periapical inflammation (INFLAM), adjacent tooth restorations (RESTOR), and the distance between the contact point and alveolar crest (DISTAN) from periapical radiographs. The system underwent rigorous validation through a four-pronged strategy: direct performance comparison against dentists of varying experience, a human-AI collaboration scenario, exploratory prospective clinical testing and multi-site validation. Results demonstrated that the AI matched junior dentists in INFLAM/RESTOR tasks while statistically outperforming experts in the DISTAN task. Crucially, human-AI integration revealed a task-specific synergistic effect, particularly in DISTAN assessments, where it significantly enhanced recall compared to isolated performance. Furthermore, an exploratory prospective clinical testing and multi-site validation demonstrated the system's consistent performance and acceptable generalization ability, achieving high specificity across diverse clinical settings. This versatile AI tool facilitates the precise, objective assessment of radiographic surrogate markers strongly associated with esthetic risk. Although direct clinical esthetic outcomes were not prospectively measured, the system's proven ability to enhance dentist performance highlights its promising potential for pre-implant evaluation and clinical decision support.