Biomaterials are in high demand due to the increasing geriatric population and a high prevalence of cardiovascular and orthopedic disorders. The combination of additive manufacturing (AM) and biomaterials is promising, especially towards patient-specific applications. With AM, unique and complex structures can be manufactured. Furthermore, the direct link to computer-aided design and digital scans allows for a direct replicable product. However, the appropriate selection of biomaterials and corresponding AM methods can be challenging but is a key factor for success. This article provides a concise material selection guide for the AM biomedical field. After providing a general description of biomaterial classes—biotolerant, bioinert, bioactive, and biodegradable—we give an overview of common ceramic, polymeric, and metallic biomaterials that can be produced by AM and review their biomedical and mechanical properties. As the field of load-bearing metallic implants experiences rapid growth, we dedicate a large portion of this review to this field and portray interesting future research directions. This article provides a general overview of the field, but it also provides possibilities for deepening the knowledge in specific aspects as it comprises comprehensive tables including materials, applications, AM techniques, and references.

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

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Porous Titanium-6Aluminum-4Vanadium scaffolds made by electron beam-based additive manufacturing (AM) have emerged as state-of-the-art implant devices. However, there is still limited knowledge on how they influence the osteogenic differentiation of bone marrow-derived mesenchymal stromal cells (BMSCs). In this study, BMSCs are cultured on such porous scaffolds to determine how the scaffolds influence the osteogenic differentiation of the cells. The scaffolds are biocompatible, as revealed by the increasing cell viability. Cells are evenly distributed on the scaffolds after 3 days of culturing followed by an increase in bone matrix development after 21 days of culturing. qPCR analysis provides insight into the cells’ osteogenic differentiation, where RUNX2 expression indicate the onset of differentiation towards osteoblasts. The COL1A1 expression suggests that the differentiated osteoblasts can produce the osteoid. Alkaline phosphatase staining indicates an onset of mineralization at day 7 in OM. The even deposits of calcium at day 21 further supports a successful bone mineralization. This work shines light on the interplay between AM Ti64 scaffolds and bone growth, which may ultimately lead to a new way of creating long lasting bone implants with fast recovery times.