A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics
Abstract
:1. Introduction
2. Stability Monitoring Method for Implant Structure
2.1. Overview of the Methodology
2.2. PZT-Based Conductance Measurement Approach
2.3. 1D CNN-Based Prediction Models
2.4. Classification Criteria
3. Local Vibrational Characteristics of PZT–Implant–Bone
3.1. Finite Element Modelling Strategy
3.1.1. Experiment on a PZT-Beam Model
3.1.2. FE Modeling of Conductance Response of Experimental PZT–Beam Model
3.1.3. Accuracy of the FE Modelling Strategy
3.2. PZT–Commerical Implant–Bone Model
3.3. Identification of Local Vibrational Modes
4. Feasibility Verification
4.1. Simulation of Conductance Response under Different Bone-Loss Levels
4.2. Stability Monitoring Using Statistical Metrics
4.2.1. Conventional Statistical Metrics
4.2.2. Stability Monitoring Result
4.3. Stability Assessment Using 1D CNN Models
4.3.1. Datasets and Setup for 1D CNN Models
4.3.2. Performance Comparison of 1D CNN-Based Bone-Loss Prediction Models
4.3.3. 1D CNN-Based Monitoring Results
5. Discussion
6. Concluding Remarks and Future Work
- (1)
- The FE modeling approach for PZT-enabled conductance sensing was successfully validated by comparing the FE modeling results with experimental data.
- (2)
- The PZT transducer activated the local circumferential modes of the implant. Some of these vibration modes were ignorable in the conductance spectrum of the implant due to the constraint of the jawbone in FE modeling.
- (3)
- Traditional statistical approaches showed their unsuitability for estimating the MBL severity at the bone–implant interface due to the nonlinearities in the conductance characteristics.
- (4)
- Among the four models, Model 3 yielded the best MBL monitoring results. The prediction and the groundtruth were consistent when evaluated on training and testing datasets, with RMSE values of only 3.704 and 4.021, respectively.
- (5)
- The proposed method offers the unique advantage of directly extracting optimal features from the raw conductance signals without the need for extensive preprocessing, rendering it highly suitable for autonomous MBL monitoring in dentistry.
- (6)
- The application of deep learning can reduce the need for high-performance impedance analyzers, allowing the use of low-cost devices for impedance-based stability monitoring of implant structures.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Tran, M.-H.; Hoang, N.-D.; Kim, J.-T.; Le, H.-K.; Dang, N.-L.; Phan, N.-T.-V.; Ho, D.-D.; Huynh, T.-C. A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics. J. Sens. Actuator Netw. 2024, 13, 52. https://doi.org/10.3390/jsan13050052
Tran M-H, Hoang N-D, Kim J-T, Le H-K, Dang N-L, Phan N-T-V, Ho D-D, Huynh T-C. A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics. Journal of Sensor and Actuator Networks. 2024; 13(5):52. https://doi.org/10.3390/jsan13050052
Chicago/Turabian StyleTran, Manh-Hung, Nhat-Duc Hoang, Jeong-Tae Kim, Hoang-Khanh Le, Ngoc-Loi Dang, Ngoc-Tuong-Vy Phan, Duc-Duy Ho, and Thanh-Canh Huynh. 2024. "A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics" Journal of Sensor and Actuator Networks 13, no. 5: 52. https://doi.org/10.3390/jsan13050052