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The Predictive Value of Resonance Frequency Analysis Measurements in the Surgical Placement and Loading of Endosseous Implants

  • Author(s): Baltayan, Serge Varoujan
  • Advisor(s): Aghaloo, Tara
  • et al.
Abstract

Implant stability is an important factor guiding the selection of placement and loading protocols. An evaluation of the currently available techniques for measuring stability clearly demonstrates a need for a non-invasive, quantitative, repeatable, and reliable way to measure implant stability over time. A potential candidate for this purpose is resonance frequency analysis (RFA).

A retrospective study was performed on implant patient data collected over a five-year period. Patients were categorized according to their placement protocol (one-stage vs. two-stage) and loading protocol (early vs. traditional). RFA measurements were recorded during placement and prior to loading. Survival or failure was determined after a minimum follow-up period of two years. Receiver operating characteristic (ROC) statistical analysis was used to determine ISQ cut-off points with respective sensitivity and specificity values for different placement and loading protocols.

In predicting implant failure, sensitivity progressively increased and specificity decreased as ISQ cut-off values increased. All failures occurred at ISQ < 66 for the placement protocol and ISQ < 67 for the loading protocol. When ISQ values were less than 60, higher survival rates were observed when implants were placed utilizing a two-stage rather than a one-stage protocol. The area under the ROC curve for placement was 0.80 (p < 0.05) and the area under the ROC curve for loading was 0.89 (p < 0.05).

RFA is a non-invasive technique used to measure the stability of implants and help guide placement and loading protocols. This study showed that increasing ISQ values correlated with increased sensitivity in detecting implant failure. Due to the high survival rates of dental implants, additional studies with an increased samples size and more implant failures are necessary to further elucidate the relationship between ISQ values and survival rates.

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