Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study

Abstract

Purpose: This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing the cementation condition between a fixed partial denture (FPD) and its abutment using a resonance frequency analysis (RFA) system.

Methods: An in vitro mandibular model was used with a single crown and three-unit bridge made of a high-gold alloy. Two cementation conditions for the single crown and its abutment were set: cemented and uncemented. Four cementation conditions were set for the bridge and abutments: both crowns were firmly cemented, only the premolar crown was cemented, only the molar crown was cemented, and both crowns were uncemented. For RFA under cementation conditions, 16 impulsive forces were directly applied to the buccal side of the tested tooth at a frequency of 4 Hz using a Periotest device. Frequency responses were measured using a 3D accelerometer mounted on the occlusal surface of the tested tooth. Both supervised and unsupervised learning methods were used to analyze the datasets.

Results: Using supervised learning, the fully cemented condition had the highest feature importance scores at approximately 3000 Hz; the partially cemented condition had the highest scores between 1000 and 2000 Hz; and the highest scores for the uncemented condition were observed between 0 and 500 Hz. Using unsupervised learning, the uncemented and partially cemented conditions exhibited the highest anomaly scores.

Conclusions: Machine learning combined with RFA exhibits good potential to assess the cementation condition of an FPD and hence facilitate the early diagnosis of FPD retention loss.