The Journal of prosthetic dentistry · 2026
Anomaly detection of screw loosening of different implant-abutment connection designs using resonance frequency analysis: An in vitro study.
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Sammour Sara Reda, Naito Hideki, Shigemitsu Ryuji, Xie Ziqi, Kimoto Tomoyuki, Sasaki Keiichi, Yoda Nobuhiro, Ogawa Toru
Аннотация
STATEMENT OF PROBLEM: Screw loosening is a common mechanical complication in implant-supported restorations, yet early detection remains challenging because of the lack of noninvasive, quantitative assessment tools.
PURPOSE: This in vitro study aimed to evaluate the ability of resonance frequency analysis (RFA) combined with machine learning techniques to detect screw loosening in 3 implant-abutment connection designs: conical, internal tri-channel, and external hexagonal.
MATERIAL AND METHODS: Three connection types were tested using 6 implants inserted into mandibular jaw models. RFA was conducted using a triaxial accelerometer under 7 loosening conditions with the frequency responses in the x, y, and z directions being recorded. Supervised learning (random forest) was used for binary (intact versus abnormal) and multiclass (7 classes) classification. Unsupervised learning was used to detect anomalies.
RESULTS: The RFA revealed unique vibrational profiles for each connection design. The random forest classifier achieved 100% accuracy in binary classification and 83% to 92% in multiclass classification. Feature importance analysis highlighted the relevance of the triaxial vibration data. The convolutional autoencoder consistently identified screw loosening as early as 5 degrees.
CONCLUSIONS: RFA combined with machine learning reliably detected screw loosening across various types of implant-abutment connections. This method offers a noninvasive, quantitative tool for the early detection of mechanical complications in implant dentistry.
PURPOSE: This in vitro study aimed to evaluate the ability of resonance frequency analysis (RFA) combined with machine learning techniques to detect screw loosening in 3 implant-abutment connection designs: conical, internal tri-channel, and external hexagonal.
MATERIAL AND METHODS: Three connection types were tested using 6 implants inserted into mandibular jaw models. RFA was conducted using a triaxial accelerometer under 7 loosening conditions with the frequency responses in the x, y, and z directions being recorded. Supervised learning (random forest) was used for binary (intact versus abnormal) and multiclass (7 classes) classification. Unsupervised learning was used to detect anomalies.
RESULTS: The RFA revealed unique vibrational profiles for each connection design. The random forest classifier achieved 100% accuracy in binary classification and 83% to 92% in multiclass classification. Feature importance analysis highlighted the relevance of the triaxial vibration data. The convolutional autoencoder consistently identified screw loosening as early as 5 degrees.
CONCLUSIONS: RFA combined with machine learning reliably detected screw loosening across various types of implant-abutment connections. This method offers a noninvasive, quantitative tool for the early detection of mechanical complications in implant dentistry.