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

Journal: Journal of prosthodontic research

PMID: 38383001

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.

Authors

Sara Reda Sammour

Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, Sendai, Japan.
Hideki Naito

Department of Civil and Environmental Engineering, Tohoku University Graduate School of Engineering, Sendai, Japan.
Tomoyuki Kimoto

Department of Electrical and Electronic Engineering, National Institute of Technology, Oita College, Oita, Japan.
Keiichi Sasaki

Tohoku University Graduate School of Dentistry, Sendai, Japan.
Toru Ogawa

Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, Sendai, Japan.

Keywords

Cementation Crowns Dental Abutments Denture Retention Denture, Partial, Fixed Humans In Vitro Techniques Machine Learning Mandible

External Resources

View on PubMed Access via DOI PubMed (38383001)

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

Abstract

Authors

Keywords

External Resources

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