Accuracy of dental implant placement using different dynamic navigation and robotic systems: an in vitro study.

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.

Correlation between peri-implant bone mineral density and primary implant stability based on artificial intelligence classification.

Currently, the classification of bone mineral density (BMD) in many research studies remains rather broad, often neglecting localized changes in BMD. This study aims to explore the correlation between peri-implant BMD and primary implant stability using a new artificial intelligence (AI)-based BMD grading system. 49 patients who received dental implant treatment at the Affiliated Hospital of Stomatology of Fujian Medical University were included. Recorded the implant stability quotient (ISQ) after implantation and the insertion torque value (ITV). A new AI-based BMD grading system was used to obtain the distribution of BMD in implant site, and the bone mineral density coefficients (BMDC) of the coronal, middle, apical, and total of the 1 mm site outside the implant were calculated by model overlap and image overlap technology. Our objective was to investigate the relationship between primary implant stability and BMDC values obtained from the new AI-based BMD grading system. There was a significant positive correlation between BMDC and ISQ value in the coronal, middle, and total of the implant (P < 0.05). However, there was no significant correlation between BMDC and ISQ values in the apical (P > 0.05). Furthermore, BMDC was notably higher at implant sites with greater ITV (P < 0.05). BMDC calculated from the new AI-based BMD grading system could more accurately present the BMD distribution in the intended implant site, thereby providing a dependable benchmark for predicting primary implant stability.

Accuracy and efficiency of robotic dental implant surgery with different human-robot interactions: An in vitro study.

OBJECTIVES: This study aims to compare the surgical efficiency (preparation and operation time) and accuracy of implant placement between robots with different human-robot interactions. METHODS: The implant robots were divided into three groups: semi-active robot (SR), active robot (AR) and passive robot (PR). Each robot placed two implants (#31 and #36) on a phantom, practising 10 times. The surgical efficiency and accuracy of implant placement were then evaluated. RESULTS: Sixty implants were placed in 30 phantoms. The mean preparation times for the AR, PR and SR groups were 3.85 ± 0.17 min, 2.14 ± 0.06 mins and 1.65 ± 0.19 mins, respectively. The mean operation time of the PR group (3.76 ± 0.59 min) was shorter that of than the AR (4.89 ± 0.70 mins) and SR (4.59 ± 0.56 min) groups (all P < 0.001). The operation time of the AR group in the anterior region (4.47 ± 0.31 min) was longer than that of the SR group (4.07 ± 0.10 min) (P = 0.007). The mean coronal, apical and axial deviations of the PR group (0.40 ± 0.12 mm, 0.49 ± 0.13 mm, 0.96 ± 0.22°) were higher than those of the AR (0.23 ± 0.11 mm, 0.24 ± 0.11 mm, 0.54 ± 0.20 °) (all P < 0.001) and SR (0.31 ± 0.10 mm, 0.36 ± 0.12 mm, 0.43 ± 0.14 °) groups (P = 0.044, P = 0.002, and P < 0.001, respectively). CONCLUSIONS: Human-robot interactions affect the efficiency of implant surgery. Active and semi-active robots show comparable implant accuracy. However, the implants placed by the passive robot show higher deviations. CLINICAL SIGNIFICANCE: This in vitro study preliminarily demonstrates that implant placement is accurate when using implant robots with different human-robot interactions. However, different human-robot interactions have variable surgical efficiencies.

The effect of implant surgery experience on the learning curve of a dynamic navigation system: an in vitro study.

BACKGROUND: Dynamic navigation systems have a broad application prospect in digital implanting field. This study aimed to explore and compare the dynamic navigation system learning curve of dentists with different implant surgery experience through dental models. METHODS: The nine participants from the same hospital were divided equally into three groups. Group 1 (G1) and Group 2 (G2) were dentists who had more than 5 years of implant surgery experience. G1 also had more than 3 years of experience with dynamic navigation, while G2 had no experience with dynamic navigation. Group 3 (G3) consisted of dentists with no implant surgery experience and no experience with dynamic navigation. Each participant sequentially placed two implants (31 and 36) on dental models according to four practice courses (1-3, 4-6, 7-9, 10-12 exercises). Each dentist completed 1-3, 4-6 exercises in one day, and then 7-9 and 10-12 exercises 7 ± 1 days later. The preparation time, surgery time and related implant accuracy were analyzed. RESULTS: Three groups placed 216 implants in four practice courses. The regressions for preparation time (F = 10.294, R2 = 0.284), coronal deviation (F = 4.117, R2 = 0.071), apical deviation (F = 13.016, R2 = 0.194) and axial deviation (F = 30.736, R2 = 0.363) were statistically significant in G2. The regressions for preparation time (F = 9.544, R2 = 0.269), surgery time (F = 45.032, R2 = 0.455), apical deviation (F = 4.295, R2 = 0.074) and axial deviation (F = 21.656, R2 = 0.286) were statistically significant in G3. Regarding preparation and surgery time, differences were found between G1 and G3, G2 and G3. Regarding implant accuracy, differences were found in the first two practice courses between G1 and G3. CONCLUSIONS: The operation process of dynamic navigation system is relatively simple and easy to use. The linear regression analysis showed there is a dynamic navigation learning curve for dentists with or without implant experience and the learning curve of surgery time for dentists with implant experience fluctuates. However, dentists with implant experience learn more efficiently and have a shorter learning curve.