3D printing in medicine最新文献

Premature loss of primary teeth is a common occurrence in pediatric dentistry and often necessitates the use of space maintainers to prevent complications. Traditional space maintainers, such as band and loop space maintainers (BLSM), have been widely used, but are fabricated using conventional methods. With advancements in technology, three-dimensional (3D) printing has emerged as a promising alternative for fabricating dental appliances including space maintainers. This study aimed to evaluate and compare the fracture strengths of conventional band and loop space maintainers (C-BLSMs) fabricated using stainless steel with that of 3D printed BLSMs manufactured using additive manufacturing techniques. Fifteen C-BLSM and fifteen 3D printed BLSMs were fabricated and subjected to fracture-strength testing using a universal testing machine. The maximum occlusal bite force in the mixed dentition was determined based on established literature. Statistical analysis was performed to compare the mean fracture resistance between the two groups. The mean fracture resistance of the 3D printed BLSMs was significantly higher (308.53 N) than that of C-BLSMs (130.85 N). This difference was statistically significant (p < 0.05), highlighting the superior mechanical properties of 3D printed BLSMs. Three-dimensional printing technology offers significant advantages in terms of fracture strength compared with conventional fabrication methods for BLSMs.

Background: Craniosynostosis is a congenital condition characterized by the premature fusion of cranial sutures, leading to potential complications such as abnormal skull growth, increased intracranial pressure, and cognitive delays. Traditionally, open cranial vault reconstruction (OCVR) has been used to treat this condition. However, it is highly subjective and greatly dependent on the surgeon's expertise, which can lead to residual deformities and the need for reoperation. Effective preoperative planning can greatly improve surgical outcomes, although the major challenge is accurately translating this plan into the clinical setting. Recently, augmented reality (AR) and 3D printing have emerged as promising technologies to facilitate this endeavor. In this work, we propose three alternatives, leveraging these technologies, to guide the precise repositioning of remodeled bone fragments in the patient.

Methods: The three guidance methods are AR on a tablet, AR with Microsoft HoloLens 2, and 3D-printed spacers. The accuracy of each method was assessed by measuring the deviation of each bone fragment from the virtual surgical plan (VSP) in a simulated environment using 3D-printed phantoms based on a 14-month-old boy with trigonocephaly. The same assessment was also performed during his actual surgery.

Results: All three guidance methods demonstrated similar levels of accuracy, with mean placement errors below 1 mm in all cases. The AR systems allowed for real-time adjustments, enhancing precision. Statistical analysis showed no significant differences in error rates between the different methods or attempts.

Conclusions: Integrating AR and 3D printing into craniosynostosis surgery holds great potential for improving OCVR. While 3D-printed spacers are useful when digital technologies are unavailable, AR-based methods provide more comprehensive guidance. Nevertheless, our study suggests that the choice may depend more on the specific clinical context, user-specific skills, and available resources rather than on a clear superiority of one method over the others.

Background: Technological constraints limit 3D printing of collagen structures with complex trabecular shapes. However, the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) method may allow for precise 3D printing of porous collagen scaffolds that carry the potential for repairing critical size bone defects.

Methods: Collagen type I scaffolds mimicking trabecular bone were fabricated through FRESH 3D printing and compared either with 2D collagen coatings or with 3D-printed polyethylene glycol diacrylate (PEGDA) scaffolds. The porosity of the printed scaffolds was visualized by confocal microscopy, the surface geometry of the scaffolds was investigated by scanning electron microscopy (SEM), and their mechanical properties were assessed with a rheometer. The osteoconductive properties of the different scaffolds were evaluated for up to four weeks by seeding and propagation of primary human osteoblasts (hOBs) or SaOS-2 cells. Intracellular alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) activities were measured, and cells colonizing scaffolds were stained for osteocalcin (OCN).

Results: The FRESH technique enables printing of constructs at the millimetre scale using highly concentrated collagen, and the creation of stable trabecular structures that can support the growth osteogenic cells. FRESH-printed collagen scaffolds displayed an intricate and fibrous 3D network, as visualized by SEM, whereas the PEGDA scaffolds had a smooth surface. Amplitude sweep analyses revealed that the collagen scaffolds exhibited predominantly elastic behaviour, as indicated by higher storage modulus values relative to loss modulus values, while the degradation rate of collagen scaffolds was greater than PEGDA. The osteoconductive properties of collagen scaffolds were similar to those of PEGDA scaffolds but superior to 2D collagen, as verified by cell culture followed by analysis of ALP/LDH activity and OCN immunostaining.

Conclusions: Our findings suggest that FRESH-printed collagen scaffolds exhibit favourable mechanical, degradation and osteoconductive properties, potentially outperforming synthetic polymers such as PEGDA in bone tissue engineering applications.

Background: Computer-assisted surgery has transformed the approach to jaw resection and reconstruction in recent years. However, the extensive time and technical expertise needed for the planning and creation of patient-specific implants and guides have posed significant challenges for many surgeons in the field. This study introduces a novel algorithm designed to streamline the traditionally intricate and time-consuming Computer-Aided Design (CAD) process for developing patient-specific implants (PSIs).

Methods: The algorithm requires a three-dimensional (3D) model of the reconstructed part. A set of points is selected along the planned location of the plate by the surgeon, defining both the geometry and the positions of the screw holes. These points are then connected to create trace lines, followed by smoothing using cubic-spline data interpolation. Subsequently, a rectangle is swept along the trace line to form the skeleton of the PSI's surface model. Screw holes are incorporated into the surface model, which is then converted into 3D printable file format. Finite element analysis is conducted to evaluate the functionality of the designed PSI.

Results: Implant design time exhibits significant reductions with the proposed algorithm, which optimizes the model files for printing. Finite Element Analysis is successfully applied to demonstrate the stress levels in the implant plate, which are within safe limits for titanium 3D-printed implants.

Conclusions: This algorithm offers a faster, more efficient, and accurate alternative to traditional CAD methods, with the potential to revolutionize the field of patient-specific implant design. Furthermore, the study demonstrates the utility of a mechanistic model for correlating patient bite force with muscle forces in the literature.

Medical image segmentation is a critical component in a wide range of clinical applications, enabling the identification and delineation of anatomical structures. This study focuses on segmentation of anatomical structures for 3D printing, virtual surgery planning, and advanced visualization such as virtual or augmented reality. Manual segmentation methods are labor-intensive and can be subjective, leading to inter-observer variability. Machine learning algorithms, particularly deep learning models, have gained traction for automating the process and are now considered state-of-the-art. However, deep-learning methods typically demand large datasets for fine-tuning and powerful graphics cards, limiting their applicability in resource-constrained settings. In this paper we introduce a robust deep learning framework for 3D medical segmentation that achieves high performance across a range of medical segmentation tasks, even when trained on a small number of subjects. This approach overcomes the need for extensive data and heavy GPU resources, facilitating adoption within healthcare systems. The potential is exemplified through six different clinical applications involving orthopedics, orbital segmentation, mandible CT, cardiac CT, fetal MRI and lung CT. Notably, a small set of hyper-parameters and augmentation settings produced segmentations with an average Dice score of 92% (SD = ±0.06) across a diverse range of organs and tissues.

Background: The maturation of 3D printing technologies has opened up a new space for patient advancements in healthcare from trainee education to patient specific medical devices. Point-of-care (POC) manufacturing, where model production is done on-site, includes multiple benefits such as enhanced communication, reduced lead time, and lower costs. However, the small scale of many POC manufacturing operations complicates their ability to establish quality assurance practices. This study presents a novel low-cost quality assurance protocol for POC 3D printing.

Methods: Four hundred specially designed quality assurance cubes were printed across four material jetting printers (J5 Medijet, Stratasys, Eden Prairie, Minnesota, USA) at two large medical centers. Three inner dimension and three outer dimension measurements as well as edge angles were measured for every cube by trained research personnel. The delta and absolute error was calculated for each cube and then compared across variables (axis, material, inner vs. outer dimension, swath and machine/site/personnel) using ANOVA analysis.

Results: Print axis and inner vs. outer dimension of the model produced statistically significant differences in error while there was no statistically significant difference in the error for material, print swath, or machine/site/personnel. For the print axes, the printers produced an average error of 26, 53, and 57 μm and the error at three sigma was found to be 100, 158, and 198 μm for the Z, R, and Theta axes, respectively.

Conclusion: This study demonstrates that this novel protocol is both feasible and reliable for quality assurance in POC 3D printing across multiple sites. This protocol offers an adaptable framework that allows users to tailor the QA process to their specific needs. Through the comprehensive method, users can measure and identify all relevant factors that might introduce error into their printed product and then follow the most critical aspects for their situation across every print. The QA cubes produced via this protocol can provide guidance on print quality and alert users to unsatisfactory machine operation which could cause prints to fall outside of engineering and clinical tolerances.

Background: Patients who undergo laparoscopic right hemicolectomy often have vascular anomalies, creating challenges for surgeons. Preoperative identification of vascular anomalies and intraoperative precise navigation can enhance surgical safety and reduce the difficulty of the procedure. Accordingly, this study aimed to explore and evaluate the application of three-dimensional (3D) reconstruction and printing technology in laparoscopic right hemicolectomy and its assistance in preoperative planning and intraoperative navigation.

Method: 11 3D-reconstructed images and printed models of right hemicolectomy vasculature were preoperatively created to assist in developing individualized surgical plans. Intraoperatively, essential vessels (gastrocolic trunk of Henle, GTH) were identified and located with the help of the 3D printed models. Additionally, 36 cases without the assistance of 3D printing were retrospectively collected for the control group. Statistical analysis was performed to evaluate the impact of the 3D printed models on surgery-related characteristics.

Results: The 3D-printed models accurately depicted anatomical structures, particularly the positions and adjacent relationships of essential vessels, including the superior mesenteric artery (SMA), superior mesenteric vein (SMV), GTH and related arterial/venous branches. The operation time was significantly lower in the 3D printing group (198.6 ± 8.8 min in 3D printing group vs. 230.7 ± 47.5 min in control group, P = 0.025).

Conclusions: In conclusion, this study represents a novel vascular 3D printed modelfor surgical planning and intraoperative navigation in laparoscopic right hemicolectomy. It underscores the potential clinical applications of 3D printing in this context. Preoperative identification of vascular anomalies and precise intraoperative navigation can feasibly reduce surgical difficulty and enhance safety.

Background: The landscape of medical education is rapidly evolving, driven by advancements in technology. This evolution has ushered in a new era characterized by digitization, connectivity, and intelligence. In this era, traditional teaching methods are being augmented with innovative technologies such as virtual learning, artificial intelligence platforms, and access to cloud-based health platforms. One notable advancement is the integration of three-dimensional (3D) reconstructed models into medical education, particularly in fields like gynecological oncology.

Methods: This study introduces 3D reconstructed models based on real cervical cancer cases as a teaching tool for undergraduate gynecological oncology education. Participants were fourth-year Clinical Medicine students of Wuhan University, China. Using student identity document numbers for grouping, half were assigned to the control group (odd numbers) and the other half to the 3D reconstructed model teaching group (even numbers). All the students completed the pre-tests before receiving traditional teaching on cervical intraepithelial lesions and cervical cancer. The control group completed the post-tests after traditional teaching alone, while the 3D reconstructed model teaching group completed the post-tests after receiving the additional 3D reconstructed model teaching. Feedback on this innovative teaching tool was collected. The pre-tests and post-tests covered cervical intraepithelial lesions, cervical cancer and staging system, and female pelvic anatomy.

Results: This study includes 267 students, with 134 in the control group and 133 in the 3D reconstructed model teaching group. The pre-test scores of the three tests between the control group and the 3D reconstructed model teaching group showed no statistical difference (p > 0.05). Compared to the control group, the post-test scores of the 3D reconstructed model teaching group in theoretical knowledge of cervical intraepithelial lesions and cervical cancer, female pelvic anatomy and 2018 International Federation of Gynecology and Obstetrics staging system for cervical cancer increased significantly (p < 0.05). Feedback from students underscored the visual benefits and engaging nature of the models, with many expressing that the 3D models provided a clearer representation of cervical cancer and enhanced their learning experience.

Conclusion: The integration of 3D reconstructed models into medical education represents a promising approach to address the complexities of teaching intricate subjects in anatomy such as gynecological oncology. These models offer a more intuitive and thorough visualization of anatomical structures and pathological processes, fostering a hands-on and exploratory learning experience for students.

Background: Anterior cruciate ligament reconstruction (ACLR) failures are associated with misplacement of the bone tunnels in up to 88%. The aim of this study is to evaluate the feasibility and accuracy of ACL tunnel placement performed with 3D printed guides.

Methods: 3D models of the femur and tibia from ten porcine specimens were reconstructed using CT scans. ACL tunnel aiming guides were created and fitted to the proximal tibial and distal femoral metaphyseal cortices. Each guide comprised two sleeves to secure the guide to the bone using Kirschner wires and one sleeve for inserting the ACL tunnel guide wire. Guides were printed using a biomedically certified resin on the in-house 3D printer. They were fixed to the antero-medial tibia/distal-lateral femur with Kirschner wires and the ACL guide wire was inserted, then the guides were removed and the ACL guide wire was drilled over. Post-operative CT scans were obtained in order to compare the actual positions of the tunnel to the planned positions. Results are presented as medians and ranges since normal distribution could not be confirmed.

Result: Median deviations between preoperative plan and actual postoperative positon were 1.15 mm (0.7-3 mm) and 0.75 mm (0.3-2.8 mm) for femoral and tibial tunnels, respectively.

Conclusion: Good accuracy of ACL tunnel placement can be achieved using 3D printed guides. Applied to a clinical setting, this technique has the potential to significantly reduce complications due to misplacement of bone tunnels.

Objective: Bioprinting is a tissue engineering technique that is rapidly evolving to include complex clinical applications. However, there is limited evidence describing how far bioprinting has progressed past the pre-clinical stage. Thus, we conducted a scoping review to assess the landscape of clinical studies, including interventional and observational trials, involving bioprinting by charting trends in general characteristics, bioprinting application, and trial design.

Methods: The term "bioprint" and its variants were searched in five trial databases (ICTRP, ScanMedicine, CENTRAL, NIHCC, HCCTD) and two registries (ClinicalTrials.gov, PHRR) on 22 February 2024. This was followed by duplicate removal and dual independent review to finalize the inclusion list. We included trials published in or translated to English mentioning "bioprint" in their design, while we excluded those that did not adhere to our definition of bioprinting. Finally, data were charted and synthesized narratively.

Results: Of 36 total search records, 11 trials met the inclusion criteria. Registration dates ranged from 2016 to 2023, with China conducting the most trials globally. Four trials had published results, while the remaining were still in progress. Four interventional trials aimed to implant bioprinted tissues made with autologous cells, including blood vessels, trachea, external ear, and wound dressings. The other seven studies were interventional and observational trials aiming to bioprint autologous cell-laden in vitro models to study conditions such as cancer.

Conclusion: Bioprinting is still in the early stages of clinical research, with a focus on producing patient-specific tissues for cancer precision medicine and regenerative purposes. More standardized reporting of bioprinting-related information is needed to improve research transparency and replicability. As the body of evidence grows, our review may be used as a framework to monitor the clinical translation of bioprinting over the years.