Debonding of coin-shaped osseointegrated implants: Coupling of experimental and numerical approaches.

While cementless implants are now widely used clinically, implant debonding still occur and is difficult to anticipate. Assessing the biomechanical strength of the bone-implant interface can help improving the understanding of osseointegration phenomena and thus preventing surgical failures. A dedicated and standardized implant model was considered.

Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy.

Osteotomies are common procedures in maxillofacial and orthopedic surgery. The surgeons still rely on their proprioception to control the progression of the osteotome. Our group has developed an instrumented hammer that was shown to provide information on the biomechanical properties of the tissue located around the osteotome tip.

Ultrasonic Evaluation of the Bone-Implant Interface.

While implant surgical interventions are now routinely performed, failures still occur and may have dramatic consequences. The clinical outcome depends on the evolution of the biomechanical properties of the bone-implant interface (BII). This chapter reviews studies investigating the use of quantitative ultrasound (QUS) techniques for the characterization of the BII.

Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network.

Although endosseous implants are widely used in the clinic, failures still occur and their clinical performance depends on the quality of osseointegration phenomena at the bone-implant interface (BII), which are given by bone ingrowth around the BII. The difficulties in ensuring clinical reliability come from the complex nature of this interphase related to the implant surface roughness and the presence of a soft tissue layer (non-mineralized bone tissue) at the BII. The aim of the present study is to develop a method to assess the soft tissue thickness at the BII based on the analysis of its ultrasonic response using a simulation based-convolution neural network (CNN).