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Dental implants are widely used in the clinic. However, there remain risks of failure, which depend on the implant stability. The aim of this paper is to compare two methods based on resonance frequency analysis (RFA) and on quantitative ultrasound (QUS) and that aim at assessing implant stability. Eighty-one identical dental implants were inserted in the iliac crests of 11 sheep. The QUS and RFA measurements were realized after different healing times (0, 5, 7, and 15 weeks). The results obtained with the QUS (respectively RFA) method were significantly different when comparing two consecutive healing time for 97% (respectively, 18%) of the implants. The error made on the estimation of the healing time when analyzing the results obtained with the QUS technique was around 10 times lower than that made when using the RFA technique. The results corresponding to the dependence of the ISQ versus healing time were significantly different when comparing two directions of RFA measurement. The results show that the QUS method allows a more accurate determination of the evolution of dental implant stability when compared to the RFA method. This study paves the way towards the development of a medical device, thus providing a decision support system to dental surgeons.

The evolution of bone tissue quantity and quality in contact with the surface of orthopedic and dental implants is a strong determinant of the surgical outcome but remains difficult to be assessed quantitatively. The aim of this study was to investigate the performance of a quantitative ultrasound (QUS) method to measure bone-implant interface (BII) properties. A dedicated animal model considering coin-shaped titanium implants with two levels of surface roughness (smooth, S a  = 0.49 µm and rough, S a  = 3.5 µm) allowed to work with a reproducible geometry and a planar interface. The implants were inserted in rabbit femurs and tibiae for 7 or 13 weeks. The ultrasonic response of the BII was measured ex vivo, leading to the determination of the 2-D spatial variations of bone in contact with the implant surface. Histological analysis was carried out to determine the bone-implant contact (BIC) ratio. The amplitude of the echo was significantly higher after 7 weeks of healing time compared to 13 weeks, for both smooth ( p  < 0.01) and rough ( p  < 0.05) implants. A negative correlation (R = − 0.63) was obtained between the ultrasonic response and the BIC. This QUS technique is more sensitive to changes of BII morphology compared to histological analyses.

Background 15 The success of cementless hip arthroplasty depends on the primary stability of the femoral stem. It remains difficult to assess the optimal number of impacts to guarantee the femoral stem stability while avoiding bone fracture. The aim of this study is to validate a method using a hammer instrumented with a force sensor to monitor the insertion of femoral stem in bovine femoral samples. Methods 20 Different cementless femoral stem were impacted into five bovine femur samples, leading to 99 configurations. Three methods were used to quantify the insertion endpoint: the impact hammer, video motion tracking and the surgeon proprioception. For each configuration, the number of impacts performed by the surgeon until he felt a correct insertion was noted Nsurg. The insertion depth E was measured through video motion tracking, and the impact number Nvid corresponding to the end of the insertion was estimated. Two indicators, noted I and D, 25 were determined from the analysis of the time variation of the force, and the impact number Nd corresponding to a threshold reached in D variation was estimated. Findings The pullout force of the femoral stem was significantly correlated with I (R\\({}^2\\)=0.81). The values of Nsurg, Nvid and Nd were similar for all configurations. 30 Interpretation The results validate the use of the impact hammer to assess the primary stability of the femoral stem and the moment when the surgeon should stop the impaction procedure for an optimal insertion, which could lead to the development of a decision support system. Abstract: 248 words. Main text: 4658 words