Preclinical Investigation of Artificial Intelligence-Assisted Implant Surgery Planning for Single Tooth Defects: A Case Series Study.

Introduction: Dental implant surgery has become a prevalent treatment option for patients with single tooth defects. However, the success of this surgery relies heavily on precise planning and execution. This study investigates the application of artificial intelligence (AI) in assisting the planning process of dental implant surgery for single tooth defects. Single tooth defects in the oral cavity pose a significant challenge in restorative dentistry. Dental implant restoration has emerged as an effective solution for rehabilitating such defects. However, the complexity of the procedure and the need for accurate treatment planning necessitate the integration of advanced technologies. In this study, we propose the utilisation of AI to enhance the precision and efficiency of implant surgery planning for single tooth defects.

Materials and methods: A total of twenty patients with single tooth loss were enrolled. Cone-beam computed tomography (CBCT) and intra-oral scans were obtained and imported into the AI-dentist software for 3D reconstruction. AI assisted in implant selection, tooth position identification, and crown fabrication. Evaluation included subjective verification and objective assessments. A paired samples t-test was used to compare planning times (dentist vs. AI), with a significance level of p < 0.05.

Results: Twenty patients (9 male, 11 female; mean age 59.5 ± 11.86 years) with single missing teeth participated in this study. Implant margins were carefully positioned: 3.05 ± 1.44 mm from adjacent roots, 2.52 ± 0.65 mm from bone plate edges, 3.05 ± 1.44 mm from sinus/canal, and 3.85 ± 1.23 mm from gingival height. Manual planning (21.50 ± 4.87 min) was statistically significantly slower than AI (11.84 ± 3.22 min, p < 0.01). Implant planning met 100% buccolingual/proximal/distal bone volume criteria and 90% sinus/canal distance criteria. Two patients required sinus lifting and bone grafting due to insufficient bone volume.

Conclusion: This study highlights the promising role of AI in enhancing the precision and efficiency of dental implant surgery planning for single tooth defects. Further studies are necessary to validate the effectiveness and safety of AI-assisted planning in a larger patient population.