Clinical evaluation of AI-based three-dimensional dental implant planning: A multicenter study.

OBJECTIVES

Dental implants have become more straightforward and convenient with advancements of digital technology in dentistry. Implant planning utilizing artificial intelligence (AI) has been attempted, yet its clinical efficacy remains underexplored. We aimed to assess the clinical applicability of AI-based implant planning software as a decision-support tool in comparison with those placed by clinicians which were clinically appropriate in their three-dimensional positions.

METHODS

Overall, 350 implants from 228 patients treated at four university hospitals were analyzed. The AI algorithm was developed using enhanced deep convolutional neural networks. Implant positions planned by the AI were compared with those placed freehand by clinicians. Three-dimensional deviations were measured and analyzed according to clinical factors, including the presence of opposing or contralateral teeth, jaw, and side. Independent sample t-test and two-way ANOVA were employed for statistical analysis.

RESULTS

The mean coronal, apical, and angular deviations were 2.99 ± 1.56 mm, 3.66 ± 1.68 mm, and 7.56 ± 4.67°, respectively. Angular deviation was significantly greater in the absence of contralateral teeth (p=0.039), and apical deviation was significantly greater in the mandible (p<0.001).

CONCLUSIONS

The AI-based 3D implant planning tool demonstrated potential as a decision-support system by providing valuable guidance in clinical scenarios. However, discrepancies between AI-generated and actual implant positions indicate that further research and development are needed to enhance its predictive accuracy.

CLINICAL SIGNIFICANCE: AI-based implant planning may serve as a supportive tool under clinician supervision, potentially improving workflow efficiency and contributing to more standardized implant treatment planning as the technology advances.