3D printing in medicine最新文献

Background: Integrating 3D printing into orthopedic oncology enables the development of patient-specific cutting guides for specific anatomy. To preserve surgical precision, especially in tumor resections where the safety margins must balance minimization of recurrence with avoidance of excessive bone removal, it is critical to maintain the dimensional accuracy of these guides throughout all stages of fabrication, disinfection, cleaning, and sterilization.

Methods: Personalized cutting guides were 3D printed using ten filaments, and 3D scanned before and after sterilization. Two sterilization methods were used: autoclave and vaporized hydrogen peroxide. Dimensional deviations were assessed by comparing the reference STL model with the scanned models using metrics such as root mean square, standard deviation, Gaussian mean, and maximum error. Pearson correlation analysis was conducted to evaluate inter-sample variability and metric interdependence.

Results: PLA and PETG showed the best dimensional accuracy in the as-printed state with RMS values of 0.093 mm and 0.093 mm, respectively, and standard deviations below 0.092 mm. After hydrogen peroxide sterilization, PETG, PC, and PETG-CF kept a high accuracy, while PLA, PLA-HP, PA, and PA6-CF showed significant deformations. Autoclave sterilization determined severe deformation in most materials, with PC showing unexpectedly changes of the geometrical form, increasing in RMS error from 0.127 mm to 3.642 mm. In the as-printed state, maximum error remained below 0.29 mm for all materials, with PLA having the highest localized deviation (0.283 mm). After hydrogen peroxide sterilization, PETG, PC, and ABS maintained maximum error values lower than 0.27 mm, while PLA increased to 0.274 mm and PLA-HP to 0.268 mm. These values, although moderate, showed geometric changes that affect fit in anatomically constrained regions. Pearson correlation analysis showed that hydrogen peroxide sterilization altered the relationship between accuracy metrics of prints after manufacturing, weakening the correlation between RMS and Gaussian mean. This suggested increased unpredictability in deformation direction and highlighted less consistent deformation patterns.

Conclusions: Disinfection and sterilization processes were highly material-dependent, as expected. PETG, PC, and PETG-CF were the most stable materials for the 3D-printed surgical guides when using cold plasma sterilization. Materials like PLA, PLA-HP, and PA require caution due to their instability. Designers should take into account the deformation directionality loss post-sterilization and integrate fit allowances into surgical guide geometry.

Background: This prospective study evaluated the efficacy of 3D-printed personalized vertebral implants in restoring spinal stability following total en bloc spondylectomy (TES) for benign spinal tumors. Given the lack of specialized implants for post-resection reconstruction, this approach integrates customized 3D-printed implants to enhance the anatomical precision, biomechanical stability, and clinical outcomes.

Methods: Four patients underwent TES using custom-designed 3D-printed vertebral implants. Key surgical parameters including operative time, intraoperative blood loss, pain reduction (VAS), and functional recovery (ODI) were assessed. Biomechanical testing was conducted to evaluate implant durability under high loads. Functional and neurological outcomes were monitored over a two-year follow-up period using clinical assessments and CT imaging.

Results: Personalized 3D-printed implants demonstrated high mechanical stability with no structural deformation under load-bearing conditions. Postoperative VAS and ODI scores significantly improved, indicating substantial pain reduction and enhanced functional recovery. Neurological evaluations revealed that 75% of patients regained full motor and sensory functions. CT imaging confirmed stable implant positioning, with no signs of subsidence, fixation failure, or implant-related complications.

Conclusions: This study highlights the clinical feasibility and potential advantages of 3D-printed personalized vertebral implants for spinal reconstruction, including optimized surgical planning, reduced operative time, and minimal blood loss. Despite promising short-term outcomes, further large-scale, multicenter trials are required to establish long-term clinical efficacy and broader applicability in diverse patient populations.

Background: Severe acetabular bone defects (Paprosky type III) pose significant challenges for reconstruction and stable implant fixation. This study aimed to analyze the biomechanical properties and clinical safety of personalized 3D-printed porous titanium alloy reinforcement augments and evaluate their therapeutic efficacy in reconstructing these complex defects.

Methods: We reviewed three cases of Paprosky type III acetabular defects reconstructed using personalized 3D-printed porous titanium alloy augments. Finite element analysis (FEA) simulated the defects, utilizing a commercial augment as a control. Stress distribution within the augments, fixation screws, acetabular cups, and surrounding bone was analyzed under simulated single-leg standing (1 × body weight), walking (4 × BW), and jogging (6 × BW) loading conditions, with comparisons made to the control.

Results: Under all loading conditions, the peak stresses observed on the augment screws and acetabular cups in all three cases were lower than the buckling strength of titanium alloy and were consistently lower than those recorded in the control group. This indicates that the personalized augments provided stable support for acetabular cup fixation, aiding in the restoration of the hip rotation center and lower limb length.

Conclusions: Personalized 3D-printed porous titanium alloy augments demonstrate favorable biomechanical safety and clinical efficacy based on FEA and initial case review. For severe acetabular bone defects, these custom augments offer good initial stability, promoting bone integration for long-term fixation, and potentially reducing risks associated with cup loosening, dislocation, and periprosthetic fracture.

Background: This study validates the intra-hospital design and 3D printing process of personalized surgical guides to enhance the accuracy of pedicle screw insertion in patients with thoracic scoliotic deformities. It introduces a novel collaborative paradigm between surgeons and engineers, aiming to improve efficiency and reduce errors in the manufacturing of patient-specific instruments (PSIs).

Methods: The process began with the generation of 3D biomodels of vertebrae from computed tomography scans. Surgical guides were then created using two 3D printing techniques: Fused Filament Fabrication (FFF) with polylactic acid (PLA) and Stereolithography (SLA) with photopolymer resin. Three different prototypes were compared based on multifactorial indicators, including economic cost, macroscopic surface finish, and mechanical stability. The mechanical performance of the guides was evaluated under loads generated during pedicle screw penetration and threading.

Results and discussions: PLA models printed using FFF were found to be cheaper and simpler to manufacture than SLA resin models. Despite differences observed under a microscope, PLA models exhibited a macroscopic surface finish comparable to that of SLA resin models. Both materials demonstrated similar mechanical properties, although their values were lower than those reported in the manufacturer's datasheet. Importantly, both types of guides successfully withstood the mechanical loads generated during surgical procedures. The intra-hospital collaboration between engineers and surgeons was identified as a key factor in improving outcomes and reducing error risks, showcasing the benefits of interdisciplinary teamwork.

Conclusions: 3D-printed PSIs made from PLA using FFF are more cost-effective and quicker to produce compared to SLA resin models, while achieving similar results in surface finish and mechanical stability. The implementation of a collaborative approach between engineers and surgeons within hospital settings enhances the efficiency and accuracy of patient-specific surgical guide manufacturing, offering a promising solution for improving surgical outcomes in thoracic scoliotic deformities.

Background: Finite Element Analysis (FEA) has evolved into a crucial tool in orthopaedic trauma research and clinical practice. This review explores the broad applications of FEA in orthopedic surgery.

Main body: FEA involves several steps, including geometry representation, segmentation, 3D rendering, meshing, material property assignment, defining boundary conditions, and specifying contact conditions. The process utilizes patient-specific volumetric data-computed tomography (CT) scan, for example-and aims for a balance between computational efficiency and accuracy. FEA provides valuable outcome measures such as stress distribution, strain quantification, fracture gap motion, failure prediction, and implant stability. These measures aid in evaluating fracture fixation techniques, implant design, and the impact of different fixation strategies. FEA has found applications in femur and proximal humerus fracture fixation, distal femur fracture planning, tibial plateau fractures, and post-traumatic osteoarthritis. It plays a pivotal role in predicting fracture risk, assessing construct stability, and informing surgical decision-making. Additionally, FEA facilitates the development of custom surgical planning and personalized implants. To enhance accuracy, FEA is combined with cadaveric biomechanical analysis, providing a reference-standard representation of in vivo kinematics. Future research should focus on refining FEA models through increased validation using cadaveric models and clinical data.

Conclusion: FEA has revolutionized orthopaedic trauma research by offering insights into biomechanics, fracture fixation, and implant design. Integration with cadaveric biomechanical analysis enhances accuracy. Further validation efforts and integration into regular clinical practice are essential for realizing FEA's full potential in individualized patient care. The combination of FEA and cadaveric analysis contributes to a comprehensive understanding of in vivo kinematics, ultimately improving patient outcomes.

Purpose: This study was aimed to design a patient-specific models of pediatric congenital choledochal cysts(CCC) for surgical simulation training.

Methods: Seventeen children suffering from CCC were included in this study. Liver and hepatic hilum structures were generated as standard parts by traditional silicone casting after 3D printing via digital imaging. Moreover, the choledochal cyst was produced as an individualized part by the silicone shaking technique and soft resin printing. Afterwards, the two model parts were assembled for laparoscopic surgical simulation. Surgical excision and suturing, and usability were evaluated. P < 0.05 was considered to indicate a significant difference.

Results: Compared with those of the digital models, the liver well and hepatic hilum structures produced were more aesthetically pleasing. Moreover, cyst models were produced accordingly. In addition, silicone models have good mechanical properties and lower costs than resins and TPU powder, and silicone models are recommended as useful tools for presurgical simulated planning. The results also showed good feedback of cutting and suturing with good face validity and usability after the simulation was complete.

Conclusions: It is feasible that the application of the silicone shaking technique can produce a hollow individualized model of CCC for surgical simulation and medical training.

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.