Influence of porosity on osteogenesis, bone growth and osteointegration in trabecular tantalum scaffolds fabricated by additive manufacturing

Porous tantalum implants are a class of materials commonly used in clinical practice to repair bone defects. However, the cumbersome and problematic preparation procedure have limited their widespread application. Additive manufacturing has revolutionized the design and process of orthopedic implants, but the pore architecture feature of porous tantalum scaffolds prepared from additive materials for optimal osseointegration are unclear, particularly the influence of porosity. We prepared trabecular bone-mimicking tantalum scaffolds with three different porosities (60%, 70% and 80%) using the laser powder bed fusing technique to examine and compare the effects of adhesion, proliferation and osteogenic differentiation capacity of rat mesenchymal stem cells on the scaffolds in vitro. The in vivo bone ingrowth and osseointegration effects of each scaffold were analyzed in a rat femoral bone defect model. Three porous tantalum scaffolds were successfully prepared and characterized. In vitro studies showed that scaffolds with 70% and 80% porosity had a better ability to osteogenic proliferation and differentiation than scaffolds with 60% porosity. In vivo studies further confirmed that tantalum scaffolds with the 70% and 80% porosity had a better ability for bone ingrowh than the scaffold with 60% porosity. As for osseointegration, more bone was bound to the material in the scaffold with 70% porosity, suggesting that the 3D printed trabecular tantalum scaffold with 70% porosity could be the optimal choice for subsequent implant design, which we will further confirm in a large animal preclinical model for better clinical use

Automatic Discoid Lateral Meniscus Diagnosis from Radiographs Based on Image Processing Tools and Machine Learning

The aim of the present study is to build a software implementation of a previous study and to diagnose discoid lateral menisci on knee joint radiograph images. A total of 160 images from normal individuals and patients who were diagnosed with discoid lateral menisci were included. Our software implementation includes two parts: preprocessing and measurement. In the first phase, the whole radiograph image was analyzed to obtain basic information about the patient. Machine learning was used to segment the knee joint from the original radiograph image. Image enhancement and denoising tools were used to strengthen the image and remove noise. In the second phase, edge detection was used to quantify important features in the image. A specific algorithm was designed to build a model of the knee joint and measure the parameters. Of the test images, 99.65% were segmented correctly. Furthermore, 97.5% of the tested images were segmented correctly and their parameters were measured successfully. There was no significant difference between manual and automatic measurements in the discoid (P=0.28) and control groups (P=0.15). The mean and standard deviations of the ratio of lateral joint space distance to the height of the lateral tibial spine were compared with the results of manual measurement. The software performed well on raw radiographs, showing a satisfying success rate and robustness. Thus, it is possible to diagnose discoid lateral menisci on radiographs with the help of radiograph-image-analyzing software (BM3D, etc.) and artificial intelligence-related tools (YOLOv3). The results of this study can help build a joint database that contains data from patients and thus can play a role in the diagnosis of discoid lateral menisci and other knee joint diseases in the future