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A forecasting model for suitable dental implantation in canine mandibular premolar region based on finite element analysis

BMC Veterinary Research volume 20, Article number: 353 (2024) Cite this article

Abstract

In recent years, dental implants have become a trend in the treatment of human patients with missing teeth, which may also be an acceptable method for companion animal dentistry. However, there is a gap challenge in determining appropriate implant sizes for different dog breeds and human. In this study, we utilized skull computed tomography data to create three-dimensional models of the mandibles of dogs in different sizes. Subsequently, implants of various sizes were designed and subjected to biomechanical finite element analysis to determine the optimal implant size. Regression models were developed, exploring the relationship between the average weight of dogs and the size of premolar implants. Our results illustrated that the regression equations for mean body weight (x, kg) and second premolar (PM2), third premolar (PM3), and fourth premolar (PM4) implant length (y, mm) in dogs were: y = 0.2785x + 7.8209, y = 0.2544x + 8.9285, and y = 0.2668x + 10.652, respectively; the premolar implant diameter (mm) y = 0.0454x + 3.3506, which may provide a reference for determine suitable clinical implant sizes for dogs.

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Background

In recent years, dental implantation has become the preferred treatment option for humans with missing teeth. Advances in implantation technology suggest that dental implantation could also be a viable option for dental care in companion animals. Osseointegration is a crucial factor that affects the success of dental implants, It involves the direct structural and functional connection between the living bone and the surface of the implant [1]. When the implant and the surrounding bone tissue form osseointegration, under the optical microscope, the implant is in direct contact with the surrounding bone tissue, without any fibrous tissue in between.This type of bone structure, distinct from fibrointegration, effectively disperses the resultant force, ensuring implant stability and reducing atrophy and absorption of the alveolar bone by integrating the artificial implant with the surrounding tissue. Even when implants are fully osseointegrated, there is a risk of bone resorption in the peri-implant regions. There are many factors affecting osseointegration, including implant diameter, length [2], material [3], surface coating [4], patient gender, age, and bone metabolic disease [1]. In addition, drug use [5], bacterial infection [6], hyperlipidemia, and low vitamin D intake [7] were also associated with bone resorption. In comparison to other factors, implant design is a modifiable factor that significantly impacts the success of implantation. Clinical practitioners must ensure that the implant size matches the patient’s jaw dimensions to optimize bone integration and enhance the implant’s long-term viability.

In dental implant research, the application of finite element analysis has the potential to mitigate the risks and expenses linked with real-world operations. Finite element analysis (FEA) is a computational technique used to predict the interaction between materials and tissues when subjected to various forces. In the broader field of engineering, FEA is utilized to address diverse problems such as stress analysis, fluid flow, electromagnetics, and heat transfer through computational models [8]. In the realm of dental implants, FEA has proven valuable in understanding the bone-implant interface [9], enabling engineers and clinicians to assess implant materials [10], designs [11], and their impact on the surrounding osseous structures. In human medicine, FEA has been employed to investigate the effects of implants with different diameters on stress distribution [12] and to identify stress variations between one-piece monophasic and two-piece dental implants supporting implant-supported prostheses [13]. In the field of veterinary medicine, FEA methods have also been utilized to determine the optimal angle for canine tooth implantation [14].

In order to improve the accuracy of finite element analysis, it is essential to establish a highly realistic finite element model. In the case of modeling the mandible, computed tomography (CT) techniques are utilized to achieve a high level of simulation [15]. In this study, we employed FEA to create mandibular bone models for each dog breed using CT data. Subsequently, implants of different sizes were designed, assuming osseointegration between the implant and the bone tissue. A biomechanical FEA was conducted to evaluate the impact of implant size on the overall implant system. The objective was to identify the most suitable implant size and provide valuable insights for the clinical selection of implant sizes for canine premolar teeth.

Fig. 1
figure 1

The building process of the model

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Results

Finite element analysis of the mandibular and implant models

Among the simulation, 37 sizes of implants (Table A.1) and 9 mandibular models for each breed were obtained for the operational performance. The results of FEA of the mandibular second premolar implant system of a standard experimental dog, beagle, are presented below as an example.

A 4-mm diameter, 11-mm long implant was used in the second premolar region of the Beagle, with the threaded portion of the implant fully embedded in cancellous bone (Fig. 2). The maximum stress (29.75 MPa) was located in the cortical bone of the implant socket. The maximum strain is in the cortical bone of the implant socket with a strain of 2218.4 microstrains.

Fig. 2
figure 2

Stress and strain distribution in surrounding bone

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Suitable size of implants for different breeds of dogs

Based on the results of finite element analysis, the appropriate size of implant was obtained by comparing the data of different sizes, and the specific data was shown in Table 1. Among them, the largest shape German shepherd has the largest implant sizes available (The second premolar implant is 5.5-mm in diameter and 18-mm in length), while the implant sizes available for the smallest beagles are relatively small (The second premolar implant is 4-mm in diameter and 11-mm in length).

Table 1 Mechanical parameters of materials related to the finite element model
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Linear regression model for prediction of implant size by the weight

A linear regression model was established with the average weight of dogs as the independent variable and implant size as the dependent variable (Fig. 3). The relationship of regression equations for mean body weight (x, kg) and second, third, and fourth premolar implant length (y, mm) in dogs were: Second premolar implant length y = 0.2785x + 7.8209, R = 0.95, P = 0.001; Third premolar implant length y = 0.2544x + 8.9285, R = 0.91, P = 0.005; Fourth premolar implant length y = 0.2668x + 10.652, R = 0.87, P = 0.010; Since the mandibular width of the premolar region varies little, within 0.5 mm, the implant diameters of the second to fourth premolar tooth are consistent in the same dog. Premolar implant diameter (mm) y = 0.0454 x + 3.3506, R = 0.81, P = 0.030.

Fig. 3
figure 3

Scatter plot of mean body weight versus premolar implant length and diameter in dogs

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Discussion

Currently, dogs are widely used in human medicine as experimental animal models to study the changes of implants post-implantation. As an international standard experimental dog, the anatomical structure of the mandible of the beagle has been studied. The gross anatomy of the mandible of Beagle revealed that the region from the second premolar tooth to the fourth premolar tooth of the mandible has a uniform bone morphology and structure, which is a very ideal site for implant placement [16]. Additionally, the fourth premolar tooth is also a common location for animal oral implant experiments [17]. Therefore, this study mainly analyzed the areas of the mandibular second premolar tooth, third premolar tooth and fourth premolar tooth suitable for implant implantation, in order to determine the appropriate implant size. Additionally, to reduce the calculation while ensuring a certain degree of accuracy, the implants designed were simplified cylindrical implants with reverse buttress thread type and 0.5 mm pitch. Davarpanah et al. [18] have studied implants using FEA illustrated that there is a significant difference between cylindrical implants with or without simplified threads. Hamidreza-Fattah et al. [19] analyzed the effects of different screw thread forms on stress distribution, including rectangular, V-shaped, buttress, and reverse buttress, the implant with a reverse buttress thread design demonstrated the lowest stress on the surrounding bone when a vertical force was applied to the implant. In the other research, Mansi Manish Oswal et al. [20] showed that implants with a pitch of 0.5 mm had a more favorable stress distribution than implants with a pitch of 1 mm. The maximum effective stress decreases gradually with decreasing thread pitch when the pitch is varied. Therefore, the implant pitch in this study was designed as reverse buttress thread type and 0.5 mm pitch.

In our study, the distribution of stresses and strains were obtained and analyzed using “von-Mises equivalent stress” and “von-Mises strain” in ANSYS. The excessive or insufficient stress and strain are not conducive to the success of the implantation. If the stress concentration exceeds the physiological limit in the bone, it can lead to increased bone resorption and a higher risk of bone loss. Conversely, underloading of the bone may cause disuse atrophy and subsequent bone loss [21, 22]. Since there is a lack of specific threshold and ultimate strength studies on canine mandible, human data were used as a reference in this study. In terms of stress, bone has a yield limit of approximately 60 MPa. Exceeding this limit can cause the accumulation of stress in the bone, resulting in fractures. However, when the stress remains below 60 MPa, any resulting bone damage can be repaired [23]. Regarding strain analysis, the optimal bone strain range for implantation is typically between 200 and 1000 microstrain. When the bone strain reaches 1000–3000 microstrain, lamellar bone formation may occur, and the maximum strain that the bone tissue can withstand is around 3000 microstrain. Beyond this threshold, bone resorption may take place [23, 24], leading to implant failure. The implant sizes obtained in this study generally ensure that the equivalent stress and strain on the mandible remain within a reasonable range, thus minimizing the risk of bone damage and promoting successful implantation. Stresses mostly were concentrated in the cortical bone of the implant socket rather than being uniformly distributed in all the bone-implant interfaces, which is similar to the findings of Oswal, Robau, Hussein, Amid et al. [20, 25,26,27]. This study also found that the peak cortical and cancellous bone stresses decreased with increasing implant diameter during vertical loading, and that increasing the length of the implant reduced the stress concentration on cortical bone, cancellous bone, and implants, which facilitated the stability of dental implants, which is consistent with previous studies [28,29,30,31].

From a biomechanical perspective, larger implant sizes should be selected whenever possible within reasonable limits, as increasing both the implant diameter and length can reduce cortical bone stress distribution in the mandible. Larger-diameter implants should be preferred over longer-length implants, since large diameter implants reduce stress more effectively than long implants [32]. However, the use of wide implants may be constrained by the width of the residual ridge and the aesthetic requirements of the natural emergence profile. It should be noted that the use of wide-diameter implants in cases with a narrow posterior ridge may potentially lead to bone loss [33]. In terms of implant system stability, it is clinically recommended to maintain at least a 1 mm cortical bone plate around the threaded part of the implant [34]. These factors limit the application of implants in small dogs. In this study, after conducting finite element analysis, it was discovered that the bone volume of toy poodles and Yorkshire terriers was insufficient to meet the requirements for implant placement due to their small mandibular size. As a result, implant sizes were only obtained for the mandibular second to fourth premolar teeth in the remaining seven breeds. Additionally, it is important to consider the factors associated with different implant strategies when evaluating implant length, particularly the presence of the mandibular canal. The mandibular canal is a crucial consideration in dental implantation. Factors such as mandibular nerve repositioning surgery, which involves partially filling the mandibular canal, may be employed to achieve stable implant placement while taking the canal into account.

Our study serves as a preliminary exploration of oral implants in companion dogs, providing initial references for potential future clinical applications. In addition, it is crucial to carefully consider the implant size based on factors such as available bone volume and the specific conditions of the case.

Conclusion

In this study, a mandibular model was constructed using CT scan data and the finite element technique, which improved accuracy and allowed for regression analysis. Our results illustrated that the regression equations for mean body weight (x, kg) and second premolar (PM2), third premolar (PM3), and fourth premolar (PM4) implant length (y, mm) in dogs were: y = 0.2785x + 7.8209, y = 0.2544x + 8.9285, and y = 0.2668x + 10.652, respectively; the premolar implant diameter (mm) y = 0.0454x + 3.3506, which may provide a reference for determining suitable clinical implant sizes for dogs. However, due to the limited data collected, the regression model is subject to individual differences, and more data is needed to improve the model’s accuracy. In biomechanical research, while the 3D finite element method has advantages that cannot be matched by other research methods, it is ultimately a simulation study with idealized material, load, and boundary condition settings. Moreover, studies on the stress levels that cause bone resorption and remodeling in actual situations are still incomplete, so the results provided by finite element analysis still need to be further validated by clinical studies. It should be emphasized that the findings of this study should serve as a guide rather than definitive recommendations.

Methods

Mandibular modeling

Sixteen CT images of the adult dog head with no mandibular damage were incorporated into our data collection, including 9 breeds (toy poodle, Yorkshire terrier, Pembroke Welsh corgi, Shiba Inu, beagle, English ancient shepherd, German shepherd, border collie, and golden retriever). All of the owners or breeders were informed and approved to participate in the study. The nine breeds include dogs of all sizes, small, medium and large breed dogs. The computed tomography images (Fig. 1A) were imported into Mimics Medical (Materialise Co., Ltd. Belgium, Version 21.0.406) in DICOM format for processing. First, the “New Mask” function was used to initially construct the three-dimensional (3D) model of both mandibles (Fig. 1B). Secondly, the “Edit Mask” function was used to erase the teeth and separate the mandibular fragments of the second premolar tooth, third premolar tooth and fourth premolar tooth region. The “Erosion” function was used to separate the cortical bone and cancellous bone. Next, the Stereolithography file was imported into Geomagic Wrap (Geomagic Co., Ltd. USA, Version 2021.0.0.3008), smoothed the surface with the “Feature Removal” function, filled the holes with the “Hole Filling” function if there are any. The “Accurate Surface” function was used to fit the surface and output the bone block model. The reconstructed cortical bone and cancellous bone were exported as Standard for the Exchange of Product Data files and imported into SOLIDWORKS (Dassault Systemes S.A Co., Ltd. France, version 29.0.0.5028) for materialization. At last, the mandibular bone block model was constructed (Fig. 1C).

Implant modeling

To minimize stress, simplified cylindrical implant models with a reverse buttress thread type (Fig. 1D) was designed using computer-aided software, SOLIDWORKS. In this study, the length and diameter of the implant were not restricted to simulate as accurately as possible. The length and diameter of the implant were adjusted according to the size of different mandibles to ensure that the implant does not touch the cortical bone. In addition, several implants of different sizes were designed for the same implant location, and the best size was obtained by comparison.

Assembly implantation modeling

The assembly was performed in SOLIDWORKS by creating a new assembly, importing the implant and mandibular bone block model, inserting the implant into the bone block (Fig. 1E), and then eliminating the interference by using the “Combine” function, and finally exporting it as an x_t file.

Finite element analysis for suitable implant size in different canine breeds

The x_t format file was imported into ANSYS Workbench (SASIP, Inc., USA, version 17.0.0.19190) for setting material properties, setting contact conditions, meshing and setting static analysis conditions. In this study, we used one-piece implants, the implant and abutment are assumed to be integrated. The material of the mandible was assumed to be a homogeneous, continuous, isotropic linear elastic material. In order to simulate the state of osseointegration, the experiment assumed that no relative displacement would occur among the implant, cortical bone and cancellous bone, and the contact type was set as “Boned” in ANSYS Workbench. The mesh of the model is divided by the “mesh” function in ANSYS(Table A2). To simulate the vertical bite of the premolar teeth, a force of 256 N (the average occlusal force of the dog) was loaded vertically on the abutment [35], fixed constraints were set on mesial and distal sides of the bone block [36] (Fig. 1F).

Since the lack of research on the elastic constants of the canine mandible, human data used in other finite element analyses were used as a reference in this study. The material property data of each part and unites were set in ANSYS Workbench (Table 2) [14, 37]. The stress cloud map of mandible was obtained by simulating calculation (Fig. 1G).

Table 2 Average weight of dogs by breed
Full size table

A forecasting regression model for suitable implant selections

A regression model was established with average dog weight (Table 3) [38,39,40,41] as independent variable and implant size as dependent variable. The regression model was built using data analysis tools “regression” in EXCEL (Microsoft Co., Ltd. USA, Version 16.75).

Table 3 Available implant sizes in the region of mandibular premolar teeth for each breed of dog
Full size table

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

FEA:

Finite element analysis

CT:

Computed tomography

3D:

Three-dimensional

PM:

Premolar teeth

PM2:

The second premolar

PM3:

The third premolar

PM4:

The fourth premolar

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Acknowledgements

We would like to thank for the support of Sichuan Science and Technology Resources Sharing Platform of Beagle Dog Breeding and Experimental Technology Service.

Funding

This work was supported by the Science and technology program of Sichuan Medical Products Administration (2022010) and Sichuan College Innovation Training Plan (S202210626133).

Author information

Authors and Affiliations

  1. College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, China

    Ruiyu Liu, Yiling Zhu, Qiaolin Zhou, Yan Luo, Yue Xie, Haifeng Liu, Zhijun Zhong, Guangneng Peng & Ziyao Zhou

  2. Sichuan Institute of Musk Deer Breeding, Chengdu, China

    Jie Yang, Ting Liang, Huan Wang & Chengli Zheng

  3. Sichuan Science and Technology Resources Sharing Platform of Beagle Dog Breeding and Experimental Technology Service, Chengdu, China

    Jie Yang, Ting Liang, Huan Wang & Chengli Zheng

  4. Chengdu Center for Animal Disease Prevention and Control, Chengdu, China

    Xiaoxiao Zhou

  5. Key Laboratory of Agricultural Bioinformatics, Ministry of Education, Sichuan Agricultural University, Chengdu, China

    Yue Xie & Ziyao Zhou

  6. College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China

    Hao Zhuang

  7. New Ruipeng Pet Healthcare Group, Chengdu, China

    Shengquan Ai

  8. Chengdu Union Beikang Pet Hospital, Chengdu, China

    Lingxue Jiang

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  1. Ruiyu Liu
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Contributions

Ruiyu Liu, Jie Yang, Yiling Zhu, Xiaoxiao Zhou, Qiaolin Zhou, Ting Liang, Huan Wang wrote the main manuscript text and Yan Luo, Yue Xie, Haifeng Liu, Zhijun Zhong, Guangneng Peng revised the manuscript, Hao Zhuang, Shengquan Ai, Lingxue Jiang prepared the CT image, Chengli Zheng and Ziyao Zhou design and supervise the project. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Chengli Zheng or Ziyao Zhou.

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The animal use protocol had been reviewed and approved by the Sichuan Agricultural University Animal Ethical and Welfare Committee (Approval No. 20210215). All methods were performed in accordance with the guidelines and regulations of protocol. All of the owners or breeders were informed and approved to participate in the study and informed consent was taken from all the owners or breeders.

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12917_2024_4221_MOESM1_ESM.docx

Supplementary Material 1: Table A1 The implants designed in this study and the stresses and strains they apply to different sites

Supplementary Material 2: Table A2 The number of elements and nodes in each part of the models

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Liu, R., Yang, J., Zhu, Y. et al. A forecasting model for suitable dental implantation in canine mandibular premolar region based on finite element analysis. BMC Vet Res 20, 353 (2024). https://doi.org/10.1186/s12917-024-04221-6

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BMC Veterinary Research

ISSN: 1746-6148

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