ABSTRACT Objectives Accurate assessment of dental implant stability is critical for predicting osseointegration outcomes and guiding clinical decision‐making. Resonance frequency analysis (RFA) is a widely adopted non‐invasive method for measuring implant stability quotient (ISQ); however, signal acquisition noise frequently compromises measurement reliability, leading to variable ISQ readings. This study aimed to develop and evaluate a deep learning–enhanced RFA framework integrating a denoising convolutional neural network (CNN) with a metadata‐aware prediction network to improve ISQ estimation accuracy and signal quality. Material and Methods A retrospective dataset of 100 implants (300 signal samples; three acquisitions per implant) was analyzed. The framework comprised: (1) a denoising CNN to suppress signal contamination and improve signal‐to‐noise ratio (SNR), and (2) a metadata‐aware prediction network estimating ISQ from denoised signals and implant‐specific parameters (bone density category and insertion torque). Performance was evaluated on a held‐out test set (20 implants, 60 samples) using MAE, RMSE, R 2 , and tolerance accuracy within ±3 ISQ units, and compared against a traditional RFA baseline. Results The denoising network reduced noise by up to 85% and improved mean SNR from 12.3 dB to 22.8 dB. The proposed model achieved MAE of 1.85 ISQ, RMSE of 2.40 ISQ, R 2 of 0.91, and tolerance accuracy of 92% within ±3 ISQ, outperforming the traditional baseline (MAE 2.65; RMSE 3.35; R 2 0.83; tolerance accuracy 77%). Conclusions The deep learning–enhanced RFA framework substantially improved signal quality and ISQ prediction accuracy over traditional RFA methods, supporting its potential in clinical implant stability monitoring. The framework should currently be regarded as a proof‐of‐concept; multi‐center prospective validation incorporating real‐world noise profiling and clinical outcome assessment is required before clinical deployment.
Cao et al. (Tue,) studied this question.