Deep Learning–Enhanced Resonance Frequency Analysis for Dental Implant Stability Assessment

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: , and tolerance accuracy within ±3 ISQ units, and compared against a traditional RFA baseline. RESULTS: 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.