Using an instrumented hammer during Summers osteotomy: an animal model

TL;DR

This study demonstrates that an instrumented hammer can predict bone damage during Summers osteotomy in an animal model, potentially increasing surgical safety by detecting damage before it occurs.

Contribution

The paper introduces a force sensor-based algorithm that detects bone damage during osteotomy before total protrusion, enhancing surgical safety.

Findings

Algorithm detected bone damage before total osteotome protrusion in 97% of cases.

Bone damage detection was within one impact of video and surgeon assessments in over 94% of cases.

The method offers a real-time prediction tool to prevent excessive impacts during surgery.

Abstract

Summers osteotomy is a technique used to increase bone height and to improve bone density in dental implant surgery. The two main risks of this surgery, which is done by impacting an osteotome in bone tissue, are i) to perforate the sinus membrane and ii) the occurrence of benign paroxysmal vertigo, which are both related to excessive impacts during the osteotomy. Therefore, impacts must be carefully modulated. The aim of this study is to determine whether an instrumented hammer can predict bone damage before the total osteotome protrusion. 35 osteotomies were performed in 9 lamb palate samples using a hammer instrumented with a force sensor to record the variation of the force as a function of time s(t). A signal processing was developed to determine the parameter $\tau$ corresponding to the time between the first two peaks of s(t). A camera was used to determine the impact number for damage: NVideo. The surgeon determined when damage occurred, leading to NSurg. An algorithm was developed to detect bone damage based on the variation of $\tau$ as a function of the impact number, leading to Ncrit. The algorithm was always able to detect bone damage before total protrusion of the osteotome. We obtained NVideo -NCrit > -2 (respectively NSurg -NCrit > -2) for 97 % (respectively 94 %) of the cases, which indicates the algorithm was almost always able to detect bone damage at most one impact after the video (respectively the surgeon). Our results pave the way to safer Summers osteotomy.