Deep Learning-Enhanced Resonance Frequency Analysis for Dental Implant Stability Assessment.

Area of Science:

• Biomedical Engineering
• Dental Implantology
• Artificial Intelligence in Medicine

Background:

• Accurate dental implant stability assessment is crucial for predicting osseointegration and guiding clinical decisions.
• Resonance frequency analysis (RFA) measures implant stability quotient (ISQ) but is often compromised by signal noise, affecting reliability.
• Existing methods lack robustness against signal acquisition noise, leading to variable ISQ readings.

Purpose of the Study:

• To develop and evaluate a deep learning-enhanced RFA framework to improve ISQ estimation accuracy and signal quality.
• Integrate a denoising convolutional neural network (CNN) with a metadata-aware prediction network for enhanced RFA.
• Overcome limitations of traditional RFA by reducing noise and improving measurement reliability.

Main Methods:

• A retrospective dataset of 100 implants (300 signal samples) was analyzed.
• A denoising CNN was employed to suppress signal noise and enhance signal-to-noise ratio (SNR).
• A metadata-aware prediction network estimated ISQ using denoised signals and implant parameters (bone density, insertion torque).

Main Results:

• The denoising network reduced noise by up to 85% and increased mean SNR from 12.3 dB to 22.8 dB.
• The proposed deep learning model achieved high accuracy: MAE of 1.85 ISQ, RMSE of 2.40 ISQ, R² of 0.91, and 92% tolerance accuracy within ±3 ISQ.
• Performance significantly outperformed the traditional RFA baseline (MAE 2.65, RMSE 3.35, R² 0.83, 77% tolerance accuracy).

Conclusions:

• The deep learning-enhanced RFA framework significantly improves signal quality and ISQ prediction accuracy compared to traditional methods.
• This framework shows potential for clinical monitoring of dental implant stability.
• Further multi-center prospective validation is needed before clinical deployment.