Search

Deacetylated chitin derivatives have been widely studied for tissue engineering purposes. This study aimed to compare the efficacy of an injectable product containing a 50% deacetylated chitin derivative (BoneReg-Inject™) and an existing product (chronOS Inject®) serving as a predicate device. A sheep model with a critical size drill hole in the tibial plateau was used. Holes of 8 mm diameter and 30 mm length were drilled bilaterally into the proximal area of the tibia and BoneReg-Inject™ or chronOS Inject® were injected into the right leg holes. Comparison of resorption and bone formation in vivo was made by X-ray micro-CT and histological evaluation after a live phase of 12 weeks. Long-term effects of BoneReg-Inject™ were studied using a 13-month live period. Significant differences were observed in (1) amount of new bone within implant (p < 0.001), higher in BoneReg-InjectTM, (2) signs of cartilage tissue (p = 0.003), more pronounced in BoneReg-InjectTM, and (3) signs of fibrous tissue (p < 0.001), less pronounced in BoneReg-InjectTM. Mineral content at 13 months postoperative was significantly higher than at 12 weeks (p < 0.001 and p < 0.05, for implant core and rim, respectively). The data demonstrate the potential of deacetylated chitin derivatives to stimulate bone formation.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

There are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients.