CHARACTERIZATION OF 3D-PRINTING FILAMENT WITH EXTRUDER MELTING TEMPERATURE VARIATION FOR BONE IMPLANT FROM KUPANG SHELL

<p><em>The number of bone damage in Indonesia continues to increase. Bone implant is one of the medical treatment methods performed on bone damage. Organic and non-organic materials can be used as bone implants. Non-organic materials are stronger, but not biocompatible, while organic materials are biocompatible, but brittle. The addition of polycaprolactone polymer (PCL) can increase the mechanical strength of 3D printing bone implant filaments. Extruder melting temperature is one of the factors that affect the quality of PCL-HAp filaments for bone implants. Studies related to temperature variations in PCL-HAp materials have not been widely studied. Therefore, it is necessary to characterize 3D printing filaments with variations in the melting temperature of the extruder as bone implants from mussel shells with temperature variables of 65^oC, 75^oC, and 85^oC. From this study, the optimum point was found at the melting extruder temperature of 75^oC with the results of a diameter of 1.810 and mechanical strength which showed an increase in tensile strength and Young's modulus of PCL-HAp composite in all variables compared to pure PCL. The SEM test showed a rough surface on the filaments that could increase the proliferation and adhesion of good cells for the growth of bone tissue.</em></p>

A Critical Review of the Design, Manufacture, and Evaluation of Bone Joint Replacements for Bone Repair

With the change of people’s living habits, bone trauma has become a common clinical disease. A large number of bone joint replacements is performed every year around the world. Bone joint replacement is a major approach for restoring the functionalities of human joints caused by bone traumas or some chronic bone diseases. However, the current bone joint replacement products still cannot meet the increasing demands and there is still room to increase the performance of the current products. The structural design of the implant is crucial because the performance of the implant relies heavily on its geometry and microarchitecture. Bionic design learning from the natural structure is widely used. With the progress of technology, machine learning can be used to optimize the structure of bone implants, which may become the focus of research in the future. In addition, the optimization of the microstructure of bone implants also has an important impact on its performance. The widely used design algorithm for the optimization of bone joint replacements is reviewed in the present study. Regarding the manufacturing of the implant, the emerging additive manufacturing technique provides more room for the design of complex microstructures. The additive manufacturing technique has enabled the production of bone joint replacements with more complex internal structures, which makes the design process more convenient. Numerical modeling plays an important role in the evaluation of the performance of an implant. For example, theoretical and numerical analysis can be carried out by establishing a musculoskeletal model to prepare for the practical use of bone implants. Besides, the in vitro and in vivo testing can provide mechanical properties of bone implants that are more in line with the implant recipient’s situation. In the present study, the progress of the design, manufacture, and evaluation of the orthopedic implant, especially the joint replacement, is critically reviewed.

Scaling the U-net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms

Highly accurate segmentation of large 3D volumes is a demanding task. Challenging applications like the segmentation of synchrotron radiation microtomograms (SRμCT) at high-resolution, which suffer from low contrast, high spatial variability and measurement artifacts, readily exceed the capacities of conventional segmentation methods, including the manual segmentation by human experts. The quantitative characterization of the osseointegration and spatio-temporal biodegradation process of bone implants requires reliable, and very precise segmentation. We investigated the scaling of 2D U-net for high resolution grayscale volumes by three crucial model hyper-parameters (i.e., the model width, depth, and input size). To leverage the 3D information of high-resolution SRμCT, common three axes prediction fusing is extended, investigating the effect of adding more than three axes prediction. In a systematic evaluation we compare the performance of scaling the U-net by intersection over union (IoU) and quantitative measurements of osseointegration and degradation parameters. Overall, we observe that a compound scaling of the U-net and multi-axes prediction fusing with soft voting yields the highest IoU for the class “degradation layer”. Finally, the quantitative analysis showed that the parameters calculated with model segmentation deviated less from the high quality results than those obtained by a semi-automatic segmentation method.