3D printing in medicine最新文献

Background: Three-dimensional (3D) printing is transforming medical education through the production of highly accurate anatomical models and personalised surgical training tools. Despite its growing influence, comprehensive bibliometric assessments in this domain remain scarce. This study aims to map the intellectual landscape and research trends of 3D printing in medical education from 2010 to 2025, offering evidence-based guidance for future innovation.

Methods: A systematic literature search was conducted in Web of Science Core Collection and PubMed for original articles and reviews related to 3D printing in medical education. CiteSpace was employed to construct and visualise collaboration, co-occurrence, and co-citation networks.

Results: The study included 302 articles from 96 institutions across 49 countries. The United States of America led in publication output, followed by China and Australia. Curtin University, the University of Toronto, and Mayo Clinic were the top three publishing institutions. The most prolific author published 11 papers, while the highest number of cited author as defined by co-citation analysis was 79. "Anatomical Sciences Education" was the most published-in and cited journal. The co-citation network analysis identified 12 thematic clusters-spanning medical modelling, anatomical education, and biomechanical testing-interconnected through pivotal high-centrality publications, illustrating the interdisciplinary expansion and evolving applications of 3D printing in medical education. Keyword analysis identified three major research hotspots: skill development and pedagogical validation, clinical surgical planning and doctor-patient communication, and emerging technologies with cross-disciplinary integration.

Conclusion: This bibliometric analysis highlights an ongoing paradigm shift in 3D printing for medical education-from initial technical exploration toward rigorous validation of educational efficacy. Current research hotspots encompass anatomical modelling, surgical simulation, and AI/AR integration. However, persistent challenges such as limited dynamic simulation capabilities, high costs, and the absence of standardised assessment frameworks hinder progress. To realise meaningful educational transformation, strengthened interdisciplinary collaboration and technological innovation are essential to advance beyond technical demonstration toward tangible pedagogical improvement.

Clinical trial number: Not applicable.

Introduction: Acetaminophen is a widely used antipyretic and analgesic treatment. Becuase oral administration poses a risk of acute liver failure, researchers are exploring alternative routes of administration using 3D printing.

Methods: This study reports a novel 3D-printed suppository using hot melt extrusion and melt deposition molding technologies. Through excipients screening, process screening and 3D printing, the production can be filtered to the most optimal state. After successfully prepared 3D printed acetaminophen suppository, the suppository's performance and pharmacokinetics profile were also evaluated.

Results: Prepared 3D printed suppository has a complete appearance, smooth interlayer stacking and qualified content determination with over 90% within 6 hours' in vitro release trend. The 3D printing acetaminophen suppository also has better release and distribution curve than the marketing acetaminophen suppository.

Conclusion: The obtained product has a complete appearance, smooth interlayer stacking and stable drug active molecules (API) at the test temperature. Melt deposition molding technologies offers a viable option for the 3D printing preparation of acetaminophen suppository.

The optimal functioning of the female reproductive system is crucial for human health, since failure frequently results in significant repercussions for fertility, sexual health, and general quality of life. These organs function through a meticulously coordinated and precisely regulated mechanism to facilitate oocyte production and embryonic development. Recently, 3D printing has become a formidable approach for producing intricate, biomimetic objects with exceptional spatial accuracy. Substantial attempts were undertaken to integrate living cells and bioactive chemicals into printed constructions for biomedical purposes. This review presents a thorough investigation of works employing 3D printing within the realm of the female reproductive system. We classified these studies based on their principal applications-tissue engineering, drug delivery, and disease modeling-and described essential data about printing methodologies, bioinks, cell types, animal models, integrated bioactive compounds, and outcomes.

Background: 3D printing' is increasingly present in the health sector. The introduction of 3D printed models into the patient care pathway can be seen as a new patient education tool based on the principle of 'see to understand'. The aim of this review is to describe studies investigating the contribution of printed models to patient care and education, and to examine their limitations. A comprehensive PubMed database search was conducted to identify relevant studies. No date, author or language restrictions were imposed. This review focused on studying the impact of a 3D organ model on 5 categories: understanding of the disease and/or the anatomy of the organ, understanding of the surgical plan and its implications, doctor-patient communication, patient satisfaction and patient anxiety. The review selected 45 articles published between 2015 and 2024. Of these, 41 articles investigated the effect of using a 3D model on understanding of the disease and/or the organ concerned. 33 articles evaluated the understanding of treatment, and the risks associated. 13 articles assessed the effect of the model on doctor-patient communication. Patient satisfaction was measured in 22 articles, and 9 articles measured patient anxiety.

Conclusion: Most of the articles analyzed-27 out of 45-demonstrate a significant enhancement in at least one category of patient education, underscoring the promising potential of 3D technology in this field. However, several methodological limitations temper these promising findings, highlighting the need for further research. Future studies should address these limitations and explore new methodologies to fully exploit 3D's potential.

Introduction: The study aimed to develop and validate a 3D-printed foot length scale for predicting intramedullary nail lengths in long bone fractures. The device utilizes the European (EU) foot length scale and regression models derived from retrospective patient data to estimate nail lengths for the femur, tibia, and humerus.

Methods and materials: The study involved two phases: (1) retrospective data collection and analysis to establish correlations between foot length and nail length, and (2) design, development, and validation of the 3D-printed device. Retrospective data were collected from 205 patients who underwent intramedullary nailing. The device was designed to measure foot length and estimate nail length based on the derived regression models. The device was prospectively validated in a clinical setting.

Results: The retrospective analysis showed strong correlations between foot length and nail length for the femur (R² = 0.98), tibia (R² = 0.91), and humerus (R² = 0.85). The prospective validation demonstrated high accuracy of the device, with mean absolute errors (MAE) of 0.67 cm, 0.74 cm, and 0.62 cm for femur, tibia, and humerus nail length predictions, respectively.

Conclusion: The 3D-printed foot length scale offers a practical and accurate method for predicting intramedullary nail lengths, potentially streamlining preoperative planning and improving surgical outcomes.

Background: The optic pathway is a complex neural structure responsible for transmitting visual information from the retina to the brain. Traditionally, the optic pathway has been depicted using two-dimensional (2D) illustrations, which, while useful for simplification, can obscure depth, orientation, and connectivity, limiting a full understanding of its three-dimensional (3D) nature which is important for surgical planning and neuroanatomy education. Due to a convergence of advancing technologies in MRI image acquisition, medical CAD and 3D illustration software, as well as 3D printing technologies, these 3D visualizations can now be physically manufactured to provide life size, patient specific, physical, color-coded 3D models. 3D models manufactured from advanced imaging can provide a more accurate, interactive, non-invasive, cost-effective alternative to medical illustration and animation than traditional dissected cadaveric anatomical specimens for both clinical and educational purposes.

Methods: The source data for this project came from both a 42 year old male patient and a 21 year old male volunteer after both had been scanned on the same seven tesla MRI including DTI for the patient and volumetric sequences for the volunteer. The model was created by segmenting the optic pathway using medical CAD software and 3D illustration software. The DTI tracts were coregistered to the anatomic brain. The model was optimized for printing and hypothetical "lesions" were added along the pathway with their corresponding visual deficits. The model was printed on an HP580 multijet fusion color printer and photorealistic eyes were printed using material jetting of photopolymer via a Stratasys J750 printer.

Results: Multiple challenges were overcome to successfully create a life size, physical, multicolor 3D printed representation of the optic pathway created from 7T MRI data.

Conclusion: This workflow resulted in a unique educational 3D representation of the human optic pathway that allows for direct manipulation, haptic feedback, and clear understanding of the anatomic relations both of this system normally and the correlations between lesion location and resultant expected visual field impairment. As opposed to the inconvenience, costs, and limited access accompanying the classical standard of advanced dissections of human specimens, this model is available to all learners in all environments.

Background: Three-Dimensional (3D) printing, also known as additive manufacturing (Linke, Additive manufacturing, explained, 2017), has rapidly emerged as a transformative tool in healthcare simulation. This scoping review investigates simulation educators' knowledge, skills, and attitudes (KSAs) about the impact of 3D printing and explores 3D printing's broader applications in healthcare simulation. By synthesizing existing literature, this study aims to identify trends, challenges, and opportunities for integrating 3D printing into simulation-based education.

Main body: The review followed the PRISMA-ScR framework, employing a six-step approach. A comprehensive search was conducted across databases, including PubMed, Medline, ERIC, CINAHL, and Google Scholar, covering studies published between 2000 and 2023. Keywords related to 3D printing and simulation-based education were used. Inclusion criteria focused on peer-reviewed articles discussing 3D printing's role in KSAs for simulation educators and its applications in healthcare simulation. Articles were charted and analyzed thematically to identify trends, challenges, and outcomes. A total of 181 studies were included, spanning 36 countries and 113 journals. Most studies focused on medical education, with 73% utilizing 3D-printed models for direct teaching. Key themes identified included realism, skill development, cost-effectiveness, and teaching effectiveness. Challenges included model accuracy, training gaps for educators, and resource limitations. Study designs were predominantly descriptive, with a significant portion being single-site case reports.

Conclusion: 3D printing has the potential to revolutionize simulation-based education by enhancing realism, accessibility, and skill development. However, gaps in educator training and methodological rigor must be addressed. Future research should focus on multi-institutional studies and long-term outcomes to maximize the impact of the technology.

Background: Additive manufacturing technology has revolutionized the medical field by enabling the production of customized implants with complex internal structures that enhance mechanical properties and biocompatibility. These intricate designs often result in exceedingly large 3D model files due to the high level of detail required. The substantial data volume poses significant file storage, transmission, and processing challenges. Traditional compression methods cannot encode complex models efficiently without compromising accuracy and compatibility. This study aims to develop a lightweight encoding strategy for 3D geometric files in medical additive manufacturing that significantly reduces file size while preserving data accuracy and compatibility with existing industry-standard formats.

Methods: We proposed a geometric relationship-based clustering method for the topological reconstruction of mesh models. The method involves non-uniform and multi-scale mesh simplification to retain critical features and reduce redundant data. By encoding these repetitive features only once, the encoding strategy enhances compression efficiency. We implemented compatible encoding schemes for the AMF (Additive Manufacturing File) and 3MF (3D Manufacturing Format) data formats, referred to as Lite AMF and Lite 3MF. Experiments on three medical implant models were conducted to evaluate the effectiveness of the proposed method.

Results: The proposed encoding strategy achieved significant file size reductions, with Lite AMF and Lite 3MF formats reducing file sizes by 81.99% and 91.34%, respectively, compared to the original formats. The compression algorithm effectively preserved the geometric characteristics of the models. The Hausdorff distance between the original and compressed models was less than 0.001 for all three models, indicating high fidelity and maintaining accuracy within the acceptable manufacturing tolerances of current medical additive manufacturing technologies.

Conclusion: The lightweight encoding strategy effectively reduces the file size of complex medical 3D models by over 80% while preserving data accuracy and compatibility with existing formats. By efficiently encoding repetitive structures and optimizing mesh data, the method enhances storage and transmission efficiency, addressing the challenges of large data volumes in medical additive manufacturing. The compatibility with standard AMF and 3MF formats ensures that the encoded models can be directly utilized in existing 3D printing software without modification.