While conventionally manufactured metallic biomaterials can hardly meet all the requirements for bone implants including complex geometry, exact dimensions, adequate biodegradability, bone-matching mechanical properties, and biological function, two additional tools have recently appeared in the arsenal of biomaterials scientists which promise to deliver the desired combination of properties. First, the unique mechanical, electrical, and biological properties of graphene (Gr) and its derivatives (GDs), e.g., a Young's modulus up to 1 TPa, can be utilized to create metal matrix composites in which GDs of varied contents (typically not more than 2 wt%), sizes (lateral sizes from a few nanometers to several micrometers), surface areas (up to the theoretical value of 2630 m²/g), and layer numbers (typically up to 10) are embedded in the biodegradable metal matrix, thereby endowing the composite implants with extraordinary properties. Second, the distinct advantages of additive manufacturing (AM) make it possible for GD-containing composite materials to precisely mimic the complex shapes and structures of bones at multiple length scales. Here, a comprehensive review of the recent advances in the development of GD-containing biodegradable metal matrix composites (GBMMCs), ranging from composite fabrication, including composite powder preparation, and AM processes, to the evaluation of AM composites in terms of their mechanical and biological properties, is presented. Furthermore, the constraints in processing composite powders, the advantages and disadvantages of applicable AM techniques, and the mechanisms of mechanical reinforcement, biodegradation modulation, osteogenesis improvement, and cytotoxicity/antibacterial balance are critically analyzed. Thereafter, the foreseeable challenges faced in the development of the next-generation of bone implants based on GBMMCs are presented and some future directions of research are identified.

Medical image analysis often involves time-consuming annotation processes. Pediatric image analysis introduces additional complexity due to the scarcity of data, noise, and growth-related anatomical variations, particularly in bone analysis, where bone structures evolve more slowly compared to other organs. This study aims to develop a segmentation model that scales across different age groups, reduces annotation effort, and ensures high accuracy, particularly in low-quality images. To address these challenges, we propose a segmentation pipeline (PedVision) that first uses a Region of Interest (ROI) network to identify relevant regions, followed by a foundation model that translates each region into meaningful instances. These instances are then mapped to segmentation classes through an instance classifier (IC) network. To initiate rounds of the training of ROI and IC networks, we developed a fast, semi-automated annotation framework that leverages foundation models to annotate a subset of images using an object-level approach. In subsequent rounds, a human discriminator selects promising predictions from the last round, which are fed by unseen data, progressively enriching the model's training dataset for further fine-tuning of the networks. The networks are expanded from low-parameter to high-parameter models across rounds, incorporating a curriculum learning approach to capture increasingly complex features. We evaluated PedVision on 552 hand X-ray images of children, retrieved from the publicly available Radiological Society of North America (RSNA) and Digital Hand Atlas (DHA) datasets, which represent a diverse range of ages and racial backgrounds. PedVision performed segmentation of 19 hand bones, grouped in five classes, and was compared against U-Net and DeepLabV3+ models using ResNet34 and ResNet101 backbones, as well as the SegFormer model with four different encoder variants. For pediatric cases (i.e., 0–7 years), the PedVision pipeline outperforms the best-performing models, achieving an 11.08 % improvement in Dice score over U-Net in the RSNA dataset and a 7.68 % improvement in the DHA dataset. When compared to DeepLabV3+, the improvements are even more substantial, with gains of 14.43 % in RSNA and 14.78 % in DHA. Additionally, PedVision shows notable advantages over the best SegFormer model, with improvements of 8.16 % in RSNA and 1.91 % in DHA. The project is open source at github.com/mohofar/PedVision.

Magnetic particles (MPs), due to their unique physical and chemical properties, have emerged as promising tools in bone tissue engineering. Their incorporation into scaffolds or uptake by bone cells, combined with exposure to external magnetic fields, has been shown in various studies to enhance cell adhesion, proliferation, and osteogenic differentiation. In this review, the state-of-the-art is presented on the synthesis processes of magnetized cells (MCs) and magnetized scaffolds (MSs), as well as the biological and mechanical effects of scaffold-free MCs, cell-seeded MSs, and MC-seeded MSs under externally applied magnetic fields on bone tissue engineering. Furthermore, the specific applications of these systems is highlighted, such as non-contact mechanical stimulation, and discuss their application to advance bone tissue engineering strategies.

OBJECTIVE: The treatment of mature biofilm in implant-associated infections (IAI) has become increasingly challenging, mainly due to the rise of antibiotic-resistant bacteria. While many antibacterial biomaterials harness their functionality through their surface properties, alternating magnetic field (AMF)-induced hyperthermia offers an approach from a fundamentally different angle. METHOD: To summarize and compare the practice of assessing AMF-induced hyperthermia in vitro and in vivo as treatment for implant-associated infections and the efficacy of this therapy, a literature search was conducted and 18 articles were selected based on relevance. RESULTS AND CONCLUSION: The studies have demonstrated that AMF-induced hyperthermia can effectively eliminate biofilms as a standalone treatment or in combination with antimicrobials. Although thermal tissue damage is an inherent concern, it can be controlled and reduced by implementing short intermittent heating patterns around 65-75ºC while still preserving antibacterial efficacy. However, clear guidelines for evaluating safety, particularly regarding thermal injury, are still lacking and should be a key focus of future work.

Two-photon polymerization (2PP) is an additive manufacturing technology capable of producing polymeric 3D nano- to mesoscale structures with design flexibility and sub-micron resolution. This study investigates the influence of 2PP printing parameters on the morphology and mechanical properties of solid and porous microstructures fabricated from three commercial resins: IP-Q, IP-S, and IP-polydimethylsiloxane (IP-PDMS). To evaluate micromechanical behavior, micropillar compression tests are conducted using IP-Q, which has not been extensively characterized. Porous structures retained 80–85% of the stiffness of solids for IP-Q and IP-S, and 50% for IP-PDMS. Fourier transform infrared spectroscopy showed degrees of conversion of 38% for IP-Q and 61% for IP-S and IP-PDMS. The optimal printing parameters for IP-Q micropillars were a laser power of 50 mW, slicing distance (s) of 1.2 μm, and hatching distance (h) of 1 μm. These settings correspond to a peak laser intensity of 1.58 × 10⁻¹¹ W cm⁻², a focal spot diameter (dxy) of 3.17 μm, a Rayleigh length (zR) of 10.13 μm, and a voxel overlap (δ) of 0.6. These conditions yielded a Young's modulus of 3.7 GPa and yield strength of 75.21 MPa. Overall, the findings emphasize the challenges of parameter optimization when introducing porosity and comparing materials. The results provide a systematic framework for tailoring 2PP processing to guide biomedical microdevice design.

The development of high-fidelity three-dimensional (3D) tissue models can minimize the need for animal models in clinical medicine and drug development. However, physical limitations regarding the distances within which diffusion processes are effective impose limitations on the size of such constructs. That is because larger-size constructs experience necrosis, especially in their centers, due to the cells residing deep inside such constructs not receiving enough oxygen and nutrients. This hampers the sustained in vitro growth of the tissues which is required for achieving functional microtissues. To address this challenge, we used three types of 3D printing technologies to create perfusable networks at different length scales and integrate them into such constructs. Toward this aim, networks incorporating porous conduits with increasingly complex configurations were designed and fabricated using fused deposition modeling, stereolithography, and two-photon polymerization while optimizing the printing conditions for each of these technologies. Furthermore, following network embedding in hydrogels, contrast agent-enhanced micro-computed tomography and confocal fluorescence microscopy were employed to characterize one of the essential network functionalities, namely the diffusion function. The investigations revealed the effects of various design parameters on the diffusion behavior of the porous conduits over 24 h. We found that the number of pores exerts the most significant influence on the diffusion behavior of the contrast agent, followed by variations in the pore size and hydrogel concentration. The analytical approach and the findings of this study establish a solid base for a new technological platform to fabricate perfusable multiscale 3D porous networks with complex designs while enabling the customization of diffusion characteristics to meet specific requirements for sustained in vitro tissue growth. Statement of significance: This study addresses an essential limitation of current 3D tissue engineering, namely, sustaining tissue viability in larger constructs through optimized nutrient and oxygen delivery. By utilizing advanced 3D printing techniques this research proposes the fabrication of perfusable, multiscale and customizable networks that enhance diffusion and enable cell access to essential nutrients throughout the construct. The findings highlighted the role of network characteristics on the diffusion of a model compound within a hydrogel matrix. This work represents a promising technological platform for creating advanced in vitro 3D tissue models that can reduce the use of animal models in research involving tissue regeneration, disease models and drug development.

Additive manufacturing (AM), also known as 3D printing, is gaining the attention of various industries as a viable alternative to conventional manufacturing, empowering design freedom, novel architectures, composition control, and sustainability. Meanwhile, metal matrix composites (MMCs) are being investigated for orthopedic implant applications due to their flexibility to achieve excellent strength, corrosion resistance, and bioactivity. Combining these two research fronts by utilizing AM for manufacturing multi-functional MMC bone scaffolds, having specific structures and compositions, has led to the recent development of a new generation of biomaterials with enhanced material properties not achievable with monolithic counterparts. Aimed at understanding the status of the research on the topic and identifying the remaining challenges, this review article discusses the utilization of AM for realizing the design vision of different MMC scaffolds, focusing on the synergistic combination of mechanical and biological characteristics, such as enhanced biodegradability, strength, and osteogenic properties. It starts by discussing the requirements for orthopedic implants and different AM techniques utilized thus far for manufacturing them, especially MMC orthopedic implants. Then, it delves into different MMCs, including Ti-, Mg-, and Fe-matrix composites that have been 3D printed into bone-substituting scaffolds and discusses their recent progress and specific characteristics. Finally, we identify the knowledge gaps and potential directions for developing MMCs further toward clinically viable, advanced orthopedic implants.

Medical devices contribute to the carbon footprint generated by the healthcare sector. The development of implants and biomaterials using recycled waste materials promotes sustainable advances in tissue engineering. Additively manufactured (AM) bone-substituting biomaterials with multifunctional properties, e.g., biodegradability, antibacterial and osteogenic potential, can contribute to sustainable healthcare. Biodegradable biomaterials eliminate secondary surgeries to remove implants, reduce post-surgical complications, and enhance patient recovery, thus decreasing the energy usage and waste associated with medical treatments. Herein, we present porous iron (Fe) scaffolds incorporating 20 vol% waste-derived eggshell particles for bone substitution. The Fe-eggshell scaffolds were fabricated using direct ink writing (DIW) technique and underwent post-AM heat treatment. During sintering, the eggshell's main component – CaCO₃, transformed into CaO. Atomic diffusion between α-Fe and CaO phases resulted in the formation of Ca₂Fe₂O₅ phase at the interface. The scaffolds were 70 % porous and displayed a biodegradation rate of 0.11 mm/year. The mechanical properties were comparable to trabecular bone and the scaffolds endured 3 million loading cycles at 0.7σ_y in r-SBF. The scaffolds showed apatite-forming ability, evidenced by the formation of (carbonaceous) hydroxyapatite, which are conducive to preosteoblast adhesion, proliferation, and differentiation. RT-qPCR analysis confirmed the osteogenic potential of the specimens as evidenced by the upregulated expression of osteopontin and osteocalcin as compared to Ti6Al4V controls. Furthermore, the scaffolds exhibited bactericidal activity (>3.9-log CFU reduction) against methicillin-sensitive and multidrug-resistant strains of Staphylococcus aureus and delayed their biofilm formation. Our research showcases the exceptional multifunctionality of DIW Fe-eggshell composite scaffolds for the sustainable development of orthopedic biomaterials. Statement of significance: We aim to improve the biofunctionalities and sustainability of biodegradable bone substitutes, by developing the extrusion-based 3D printed porous Fe composite scaffolds containing eggshell-derived CaO bioceramics. Our results demonstrated that Fe-eggshell scaffolds exhibited hydroxyapatite-forming ability in simulated body fluid, having mechanical properties in the range of trabecular bone even after 4 weeks biodegradation, supported the proliferation of preosteoblasts and upregulated the expression of osteogenic genes. Moreover, the scaffolds were bactericidal against methicillin-sensitive and multi-drug resistant strains Staphylococcus aureus and delayed their biofilm formation.

High-performance soft–hard interfaces are inherently difficult to fabricate due to the dissimilar mechanical properties of both materials, especially when connecting extremely soft biomaterials, such as hydrogels, to much harder biomaterials, such as rigid polymers. Nevertheless, there is significant clinical demand for synthetic soft–hard interfaces. Here, soft–hard interface geometries are proposed, designed with the aid of computational analyses and fabricated as 3D-printed hydrogel-to-polylactide (PLA) structures. Two primary interlocking geometries (i.e., anti-trapezoidal (AT) and double-hook (DH)) are used to study the envelope of 2.5D geometric interlocking designs, fabricated through hybrid 3D printing, combining pneumatic extrusion with fused deposition modeling. Finite-element analysis, uniaxial tensile tests, and digital image correlation (DIC) are used to characterize the geometries and identify parameters that significantly influence their mechanical performance. These findings reveal significant differences between geometric designs, where DH geometries performed significantly better than AT geometries, exhibiting a 190% increase in the maximum force, F_max, and a 340% increase in the fracture toughness, W. Compared to the control groups (i.e., flat, inset, and 90° interfaces), F_max and W values increased by 500%–990% and 350%–1200%, respectively. The findings of this study can serve as a guideline for the design and fabrication of efficient soft–hard interfaces with performances close to predicted values.

Living biological tissue is a complex system, constantly growing and changing in response to external and internal stimuli. These processes lead to remarkable and intricate changes in shape. Modeling and understanding both natural and pathological (or abnormal) changes in the shape of anatomical structures is highly relevant, with applications in diagnostic, prognostic, and therapeutic healthcare. Nevertheless, modeling the longitudinal shape change of biological tissue is a non-trivial task due to its inherent nonlinear nature. In this review, we highlight several existing methodologies and tools for modeling longitudinal shape change (i.e., spatiotemporal shape modeling). These methods range from diffeomorphic metric mapping to deep-learning based approaches (e.g., autoencoders, generative networks, recurrent neural networks, etc.). We discuss the synergistic combinations of existing technologies and potential directions for future research, underscoring key deficiencies in the current research landscape.

Biocompatible and shape-morphing metallic structures have been proposed for musculoskeletal applications to provide structural support to bony tissues. However, fabricating these structures to conform to a wide range of curvatures, including both single and double curvatures, remains a significant challenge. In this study, we present and analyze structures featuring a regular tiling network connected by spherical joints, forming a chain mail-like mechanism capable of adapting to complex geometries with clay-like flexibility. Simulations using a multibody kinematics model show that parameters such as unit cell shape, dimension ratios, and substrate curvature affect the shape-matching abilities of the structure. Experimental validation using specimens additively manufactured through laser-based powder bed fusion (from Ti6Al4V) and full-field strain measurements performed through digital image correlation confirms the simulation results, demonstrating that reducing structural density (i.e., fewer bodies, struts, and joints per unit area) improves shape adaptability. However, the improved shape morphing capability often comes at the expense of mechanical strength under uni-axial tensile loads. These findings provide a framework for optimizing structures designed to achieve efficient surface conformance and adaptability in load-bearing applications.

Additively manufactured (AM) iron (Fe)-based scaffolds have been developed as promising biodegradable bone-substituting biomaterials. Multi-material extrusion-based 3D printing has recently yielded Fe-manganese (Mn) alloy-based scaffolds that can resolve ferromagnetism and cytotoxicity associated with Fe-based biomaterials. Herein, we, for the first time, present the findings from in vivo study on extrusion-based AM FeMn-akermanite (Ak) scaffolds for critical-size bone defect repair. The scaffolds comprised Fe, 35 wt% Mn, and 20 or 30 vol% Ak, with microporous struts and 61–63 % porosity. Both scaffolds exhibited mechanical properties within the range of trabecular bone and provided suitable sites for Ca/P deposition during in vitro biodegradation. In vitro cell cultures demonstrated favorable cell responses without negating the osteogenic potential of cells. An in vivo study was conducted in a murine semi-orthotopic subcutaneous model. With this model, 4 bovine bone plugs were implanted subcutaneously with critical-size defects created at their cores. Scaffolds were placed into these critical-size defects to assess biodegradation and bone formation. After 16 weeks, the volume of scaffolds decreased by 6–8 %. The FeMn-20Ak scaffolds retained their yield strength and elastic modulus during the 16 weeks in vivo, whereas the mechanical integrity of FeMn-30Ak scaffolds deteriorated after mechanical push-out tests. Excellent osseointegration of both scaffold groups was apparent. 3D reconstruction of CT images revealed that FeMn-30Ak scaffolds had more newly formed tissue in the macro-pores than FeMn-20Ak. Altogether, our findings demonstrate the potential of AM FeMn-Ak scaffolds as biodegradable bone substitutes, encouraging further in vivo research in a large animal model.

Existing deep learning (DL) networks are primarily trained on adult datasets and may not always generalize to pediatric populations, where growth plays a major role. Here, we investigated improving semantic segmentation outcomes of pediatric hand phalanges from radiographs without relying on fully pediatric training datasets, which are scarce. First, alternative DL networks (FCN-8, FCN-32, U-Net, Inception U-Net, and DeepLabv3+) were trained with manually segmented radiographs of near-skeletally-mature (NSM) subjects and their performances were evaluated using mean intersection-over-union (Mean IoU) and multiclass Dice scores. DeepLabv3+ and Inception U-Net performed the best for NSM segmentation, with Mean IoU scores of 0.899 ± 0.035 and 0.887 ± 0.062, respectively. These networks were then used to investigate zero pediatric data (scaling-based data augmentation) and minimal pediatric data (incremental pediatric data substitution) approaches to improve age-domain generalizability. The minimal pediatric data approach proved effective, with a 20 % pediatric data inclusion leading to an up to 21.1 % increase in Mean IoU for pediatric subjects compared to networks trained exclusively on NSM subjects. Furthermore, no adverse effects of this approach were found when tested on NSM subjects, and there were even improvements in performance for Inception U-Net. To conclude, we highlight that networks utilizing multi-scale filters perform best for the semantic segmentation of hand phalanges. We further demonstrate that a minimal inclusion of pediatric training data can markedly improve age-domain generalizability for semantic segmentation tasks. This removes the difficult task of gathering large training datasets of pediatric subjects, which is often impractical, if not impossible.

Organoids are innovative three-dimensional and self-organizing cell cultures of various lineages that can be used to study diverse tissues and organs. Human organoids have dramatically increased our understanding of developmental and disease biology. They provide a patient-specific model to study known diseases, with advantages over animal models, and can also provide insights into emerging and future health threats related to climate change, zoonotic infections, environmental pollutants or even microgravity during space exploration. Furthermore, organoids show potential for regenerative cell therapies and organ transplantation. Still, several challenges for broad clinical application remain, including inefficiencies in initiation and expansion, increasing model complexity and difficulties with upscaling clinical-grade cultures and developing more organ-specific human tissue microenvironments. To achieve the full potential of organoid technology, interdisciplinary efforts are needed, integrating advances from biology, bioengineering, computational science, ethics and clinical research. In this Review, we showcase pivotal achievements in epithelial organoid research and technologies and provide an outlook for the future of organoids in advancing human health and medicine.

This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field.

In this study, by mimicking bone tissue adaptation, we introduce a hierarchical design approach to optimize the mechanical performance of auxetic metamaterials. Using a multi-material voxel-based 3D printer, we rationally position soft and hard phases at the voxel level based on a bone-inspired remodeling algorithm that maintains the homeostasis of strain energy density. This process introduces an additional length scale within the structure, leading to (1) a significant expansion of the envelope of achievable elastic properties, (2) more homogeneous strain energy distributions, and (3) up to 78 % stronger metamaterials compared to initial designs. Our results demonstrate that this bone-mimicking design approach enables the emergence of an intermediate length scale between the unit cell and voxel scales, which is responsible for the observed improvements in mechanical performance. Taken together, these findings highlight the potential of biomimetic remodeling for the rational design of stress-worthy, multiscale mechanical metamaterials that combine unusual elastic properties with high mechanical performance.

Additively manufactured (AM) biodegradable zinc alloys hold huge potential as promising candidates for bone defect and fracture repair, thanks to their suitable biodegradation rates and acceptable biocompatibility. However, the mechanical properties of AM zinc alloys developed so far, ductility in particular, fall short of the requirements for bone substitution. Here, we present Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures, fabricated using laser powder bed fusion (LPBF). Notably, the LPBF Zn-Mn-Mg alloy exhibited an extraordinary balance of strength and ductility, with an ultimate tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, outperforming all previously reported AM zinc alloys. The simultaneously enhanced strength and ductility of the ternary alloy were attributed to the strong grain-refining effect of the Mg₂Zn₁₁ second phase and the synthetic strengthening caused by the dispersion of the MnZn₁₃ and Mg₂Zn₁₁ second phases inside the grains and at the grain boundaries. In addition, both alloys had similar rates of in vitro biodegradation (∼0.15 mm/year), properly aligned with the bone remodeling process, while also demonstrating favorable biocompatibility and upregulating multiple osteogenic markers. The Zn-Mn-Mg alloy showed even better osteogenic potential than the Zn-Mn alloy, owing to the addition of Mg. The combined attributes of the LPBF Zn-Mn-Mg ternary alloy indicated huge potential for its use as a bone repair material, especially for load-bearing bone fixation. Statement of significance: The mechanical properties of previously developed additively manufactured biodegradable zinc alloys, especially ductility, have not met the requirements for bone repair. Using laser powder bed fusion (LPBF), we fabricated Zn-1Mn and Zn-1Mn-0.4Mg alloy implants with unique microstructures. The LPBF Zn-Mn-Mg alloy demonstrated an exceptional balance of strength and ductility, achieving a tensile strength of 289 MPa, yield strength of 213.5 MPa, and elongation over 20 %, surpassing all reported AM zinc alloys. This study is the first to produce a directly printed biodegradable alloy meeting the mechanical requirements for bone fixation devices without post-processing. Additionally, the alloy exhibited moderate a biodegradation rate and excellent biocompatibility, underscoring its potential for load-bearing bone repair applications.

Tissue engineering approaches for cartilage tissue regeneration are expanding to include the complex features of the tissue, such as the biological and mechanical gradients. Many of these approaches are, however, based on the use of multiple biomaterials or concentrations, and crosslinking methods that make it difficult to integrate and control the properties of the resulting scaffolds. In this study, a 3D bioprinted scaffold with a stiffness gradient was fabricated by using a single biomaterial type and concentration combined with a directional ionic crosslinking method. The scaffolds revealed a gradient in stiffness from 39.8 ± 6.6 kPa at the top to 60.6 ± 10.9 kPa at the bottom of the scaffolds. Live/dead analysis of human chondrocytes embedded in the scaffolds showed no negative effects of the stiffness gradient on cell viability over 28 days. The induced stiffness gradient led to a gradient in cell density and sulfated glycosaminoglycan deposition in the bioprinted tissue constructs with enhanced values in the softer top region of the scaffolds as compared to the stiffer bottom part. This study showed a novel method to generate scaffolds with stiffness gradients from a single biomaterial and indicates that such scaffolds could be used to spatially regulate the behavior of chondrocytes and the associated deposition of the cartilage matrix.

Acetabular defects pose significant challenges in orthopedic surgery, particularly in revision total hip arthroplasty (THA). Here, we design, additively manufacture, and evaluate shape-morphing porous implants with kinematic structures to address these defects. Three defect types were examined using synthetic hemipelvis models: posterior wall, cranial-posterior combination, and central-posterior defects. The implants were secured with screws and bone cement, and their surface conformity was assessed through micro computed tomography (µCT). Biomechanical performance was evaluated under quasi-static compression and cyclic loading conditions. Results demonstrated high surface conformity of the flexible mesh across all defect types, with minimal differences from healthy acetabula (< 10 mm). The mesh implants exhibited strong load-bearing capacity, with failures occurring only in the pubic region of the hemipelvis, while both the implants and mesh-cement interfaces remained intact. The implants withstood cyclic loading simulating half the body weight of a 80 kg patient for >1000,000 loading cycles with no evidence of fatigue failure, further confirming their durability. These findings suggest that the flexible mesh implant provides a potential solution for complex acetabular defects, offering anatomical conformity and mechanical stability, even in cases where conventional mesh grafts might be inadequate. Future studies, including cadaveric testing and clinical trials, are necessary to further validate these results in (pre-)clinical settings. Statement of significance: This study addresses the need for adaptable solutions to complex acetabular defects in revision total hip arthroplasty (THA). Traditional implants struggle to conform to severe bone loss and irregular geometries, risking suboptimal fit, and implant migration. We introduce a 3D-printed, shape-morphing porous implant with kinematic structures, offering high anatomical conformity, mechanical robustness, and support for bone graft integration. Combining the adaptability of patient-specific implants with the efficiency of standard designs, this implant reduces lead times while enabling a tailored fit. This innovative approach provides a reliable solution for managing complex defects, addressing limitations of conventional implants, and improving outcomes in orthopedic reconstruction.

Mechanical characterization of three-dimensional (3D) printed meta-biomaterials is rapidly becoming a crucial step in the development of novel medical device concepts, including those used in functionally graded implants for orthopedic applications. Finite element simulations are a valid, FDA-acknowledged alternative to experimental tests, which are time-consuming, expensive, and labor-intensive. However, when applied to 3D-printed meta-biomaterials, state-of-the-art finite element modeling approaches are becoming increasingly complex, while their accuracy remains limited. A critical condition for accurate simulation results is the identification of correct modelling parameters. This study proposes a machine learning-based strategy for identifying model parameters, including material properties and model boundary conditions, to enable accurate simulations of macro-scale mechanical behavior. To achieve this goal, a physics-informed artificial neural network model (PIANN) was developed and trained using data generated through a fully automated finite element modeling workflow. Subsequently, the PIANN model was then tested using real experimental force-displacement data as its input. The experimental data from 3D-printed structures were used to predict the associated parameters for finite element modeling. Finally, the workflow was validated by qualitatively and quantitatively comparing simulation results to the experimental data. Based on these results, we concluded that the proposed workflow could identify model parameters such that the predictions of associated finite element simulations are in agreement with experimental observations. Furthermore, resulting finite element models were found to outperform state-of-the-art models in terms of both quantitative and qualitative accuracy. Therefore, the proposed strategy has the potential to facilitate the broader application of finite element simulations in evaluating 3D-printed parts, in general, and 3D-printed meta-biomaterials, in particular.