REVOLUTIONIZING 3D PRINTING THROUGH MACHINE LEARNING: POTENTIAL AND CHALLENGES IN BIOPRINTING

Three-dimensional (3D) printing is one of the most well-liked new innovative and promising manufacturing techniques, which has demonstrated tremendous potential for the creation of biostructures in tissue engineering, particularly for bones, orthopaedic tissues, and related organs. 3D printing for the medical industry was considered a lofty pipe dream. Time and money, though, made it a reality. Today's 3D printing technology has a significant possibility to assist pharmaceutical and medical corporations in developing more specialised pharmaceuticals, enabling the quick creation of medical implants, and transforming how doctors and surgeons approach surgical planning. In today's practise of precision medicine and for individualised therapies, patient-specific anatomical models that are 3D printed are becoming increasingly helpful tools. In contrast to the conventional use of 3D printing to create cell-free scaffolds, 3D bioprinting requires various technical methods, such as biomimicry, autonomous self-assembly, and mini-tissue building blocks, to create 3D structures with mechanical and biological properties suitable for the deposition of living cells and the restoration of tissue and organ function. Cells, bioinks, and bioprinters are all necessary components of the bioprinting process, and each one of them has biological, technological, ethical, and cost- and clinically-effectiveness-related issues. As a result, there are several difficulties in integrating 3D bioprinting into widespread clinical practise. Currently, there are multiple applications for 3D bioprinting such as in surgery, cardiovascular system, musculoskeletal and even in drug screening. All of which will be discussed in this review.

The application of three-dimensional (3D) printing in medicine has increased rapidly in the last few years. Various new commercial 3D printers and 3D bioprinters are introduced into the current market. 3D printing scientific community has witnessed a rapid growth in the application of 3D printing for various medical needs. There has been a research advancement in 4D printing with a widened scope of 3D bioprinting. The growing demand for 3D printing and its use for commercial and industrial purposes in medical technology have facilitated the need for introduction of new international standards and regulations. The need for organ replacement has generated curiosity and competition among researchers to race for organ 3D printing, which can be identified with increased publications and patents that are filed in recent years. Hence, there is a need to update the readers with the latest information. The second edition of this chapter updates the contents of first section to fourth section with current information and some modified pictorial representations. New section describes the relevance of the new chapters added to this edition and updates the readers with insights on advancement of 3D printing in the last decade. Briefly, this chapter introduces the reader with a brief history of 3D printing in medical technology, components of 3D printing, 3D bioprinting, organ 3D bioprinting, advantages of 3D printing with current challenges and commercially available 3D printers in the current market. New sections introduced in this edition detail about the ASTM and ISO standards that are used for 3D printing in medicine, the regulatory framework with current challenges, ethical and social concerns with the current 3D printing scientific community, and the importance of intellectual property in safeguarding the interests of the innovators.

Traditional 3D printing usually uses professional software such as SolidWorks and Maya for modeling. However, applying the software mentioned above to customize 3D food printing patterns has the problems of a solid professional operation and high skill requirements. In response to this problem, a pattern drawing software applied to 3D food printing equipment was designed. The software uses image processing technology and C# programming language, based on Windows operating system and. NET Framework, combined with SQLite database technology to achieve product pattern drawing and model export functions. Among them, the automatic extraction of the enclosed area realized by the flood filling algorithm is the core function of product pattern drawing; the automatic acquisition of the contour of the connected graph recognized by the canny edge detection algorithm is the crucial step of the model derivation. The test results show that the software can quickly implement template import, pattern drawing, model export, and other operations, which improves the efficiency and ease of operation of pattern drawing in 3D food printing; The model file exported by the software can be directly used in the layered printing process of a 3D printer, which provides a direction for promoting the application of 3D printing technology in the food industry.

Three-dimensional (3D) printing technology is characterized by "inside-out" stack manufacturing. Compared with conventional technologies, 3D printing has the advantage of personalization and precision. Therefore, the shape and internal structure of the scaffolds made by 3D printing technology are highly biomimetic. Besides, 3D bioprinting can precisely deposit the biomaterials, seeding cells and cytokines at the same time, which is a breakthrough in printing technique and material science. With the development of 3D printing, it will make great contributions to the reconstruction of vertebrae and intervertebral disc in the future.

The Internet of Things (IoT) is well known for being a major source of Big Data since it builds on the connection to the Internet of a huge number of smart devices that continuously report the status of their physical environments. Although the IoT paradigm is focused on the connection of objects, its real potential lies not in the objects themselves, but in the ability to generate valuable knowledge from the data extracted from these objects. It can be said that the Internet of Things is actually not about things , but about data . In this context, machine learning (ML) is a promising technique to process the generated data and transform them into information, knowledge, predict trends, produce valuable insights, and guide automated decision‐making processes. However, the use of ML techniques in IoT brings up several challenges, especially regarding the computational requirements demanded by them. Data produced by IoT devices can be typically processed in three different layers: in the data sources (Things layer), in computational clouds, and in the intermediate layer known as fog or edge. Depending on the application quality of service (QoS) parameters (such as response time) and the complexity of the required processing, each layer will be more suitable for performing a given ML technique. The overhead imposed by current ML techniques on devices of the fog and things layers, where resources are scarce, hinders their widespread adoption in this context. Recent developments in running ML algorithms on fog devices show that, due to their limited hardware and power supply, the decision‐making process in the fog is not an easy task. On the other hand, a decision‐making process built upon information extracted from several devices on the network is more reliable than by taking some limited view of the context. This chapter presents the challenges to perform big data/big stream analytics in fog computing using the recent artificial intelligence developments.

Bone defect repairing and reconstruction has been a hot research topic in orthopedics for a long time. Tissue engineering and stem cell technology have made a series of important achievements in promoting bone regeneration to treat bone defect. In recent years, 3D bio-printing, which combining with 3D printing, tissue engineering and stem cell technology, has significant advantages in optimizing the geometry, mechanical properties and biological functions of repairing tissue for bone defect by accurately controlling the shape and internal structure of scaffolds, and printing biomaterials, stem cells and (or) cells into three-dimensional biological functional structures. A series of important progress has been achieved. The common printing methods for bio-printing related to orthopedic include: Inkjet 3D bioprinting; microextrusion 3D bioprinting; laser-assisted 3D bioprinting; stereolithography; microvalve based 3D bioprinting. Various printing methods and principles are not the same, and each has advantages and disadvantages, and the applicable bio ink is also different. The key technologies of orthopedic bio-3D printing include: the methods of image data acquisition and 3D structure design; development and application of composite bio-scaffold materials suitable for 3D printing, tissue engineering and bone-enhancing effect; stem cell selection for ensuring graft biological performance and induced pluripotent stem cell technology; in vitro bioreactor technology for improving the maturity and biocharacterization of bioprinted tissues. The literature published in the field of biological 3D printing research has continued to grow at a high rate since 2008. Using the bibliometric analysis software VOSviewer to create a co-word matrix for high-frequency keywords and to draw a keyword co-occurrence network map analysis, biological 3D printing research hotspots are the use of tissue engineering methods to 3D printed tissue scaffolds, while studying cell survival and drug effects. The instruments and methods of bio-3D printing are also one of the research hotspots.

Fraud in the financial sector, e-commerce, and online services is becoming increasingly frequent and sophisticated. Traditional rule-based systems, while still helpful in detecting known fraud patterns, struggle to keep up with new, evolving attack vectors, as static rules are quickly circumvented. In contrast, machine learning (ML) provides a dynamic and scalable approach that can process vast amounts of transactional and behavioral data to identify subtle anomalies and suspicious activity.This paper provides a comprehensive overview of current ML techniques used in fraud detec-tion, categorized into three main groups: classification models, anomaly detection methods, and deep learning architectures. It discusses real-world applications across various fraud scenarios, including credit card abuse, account takeovers, cybercrime, and scams in digital comerce.Emphasis is placed on the strengths and limitations of each approach, with attention to real-world considerations like scalability, model transparency, and the challenge of class imbal-ance. The paper also reviews recent advances, including graph-based representations of fi-nancial interactions, IP-based behavioral profiling, and the emergence of hybrid systems that integrate multiple ML techniques – such as combining autoencoders with boosting algorithms for improved accuracy, especially when labeled data is scarce.The findings aim to support the development of flexible, high-performance fraud detection solutions that leverage the most effective ML practices and capitalize on the synergy of hybrid model architectures.

PurposeSkin cancer is the most common tumor in the population. There are different therapeutic modalities. Brachytherapy is one of the techniques used, in which it is necessary to build customized moulds for some patients. Currently, these moulds are made by hand using rudimentary techniques. We present a new procedure based on 3D printing and the analysis of the clinical workflow.Material and methodsMoulds can be made either by hand or by automated 3D printing. For making moulds by hand, a patient’s alginate negative is created and, from that, the gypsum cast and customized moulds are made by hand from the patient’s negative template. The new process is based on 3D printing. The first step is to take a 3D scan of the surface of the patient and then, 3D modelling software is used to obtain an accurate anatomical reconstruction of the treatment area. We present the clinical workflow using 3D scanning and printing technology, comparing its costs with the usual custom handmade mould protocol.ResultsThe time spent for the new process is 6.25 hours, in contrast to the time spent for the conventional process, which is 9.5 hours. We found a 34% reduction in time required to create a mould for brachytherapy treatment. The labor cost of the conventional process is 211.5 vs. 152.5 hours, so the reduction is 59 hours. There is also a 49.5% reduction in the financial costs, mostly due to lack of need of a computed tomography (CT) scan of the gypsum and the mould. 3D scanning and printing offers financial benefits and reduces the clinical workload.ConclusionsAs the present project demonstrates, through the application of 3D printing technologies, the costs and time spent during the process in the clinical workload in brachytherapy treatment are reduced. Overall, 3D printing is a promising technique for brachytherapy that might be well received in the community.

3D Printing, sometimes referred to as additive manufacturing, has made the concept of personalized medicine a reality. The primary objective of 3D and 4D printing is to produce intricate, customized pharmaceuticals at a reasonable cost. With improvements in materials, resolution, and speed, 3D printing technology is quickly developing. It includes faster construction, cost efficiency through reduced waste, design flexibility for complex structures, and sustainability through optimized material usage. An extensive literature survey was done on 3D and 4D printing of pharmaceuticals using PubMed, Elsevier, ScienceDirect, and Springer. The results were then filtered based on the titles, abstracts, and accessibility of the complete texts. The search engine Google Scholar was accessed for literature data mining. From the data mining, it was found that from the year 2009 to 2024 the number of research publications surged more than 200 times on the current topic. Even though 3-D and 4-D printing technologies have advanced significantly in a short amount of time, the most often used ones are still stereolithography, nozzle-based deposition, inkjet, and selective laser sintering. Their use has been modified for the production of nanoparticles, polypills, tablets, and implants, etc. Pharma's aspirations for tailored medications are being revolutionized by 3D printing, but cost, flexibility, and bioequivalence still need to be investigated. The present review offers a thorough analysis of various 3D and 4D printing methods and emphasizes the major advantages and disadvantages and major key challenges of 3D and 4D printing related to pharmaceuticals. Compared to 3D Printing, 4D printing offers better quality, efficacy, and functionality.

Three-dimensional (3D) bioprinting is a versatile technique for biomedical applications, and includes organ printing, 3D disease model development, and drug delivery. The bioprintable materials combined with live cells have been utilized as bioinks in 3D bioprinter to fabricate versatile 3D printed structures. The 3D structures developed with smart and responsive materials can change their dimension, a technique similar to self-assembly, unfolding a new branch termed as four-dimensional (4D) printing. This manuscript reviews the details of various bioprintable materials and 3D printers, the application of 3D printing in biomedicine, smart materials, and stimulations for 4D printing. Further, this article also summarizes the regulatory issues and the limitations involved with the bioprinting. The advancements in 3D and 4D printing technology have significantly contributed to the medical field, and adequate research and amalgamation of engineering and science ideas will strengthen the application of this technology and bring solution for the existing problems.Graphical abstract

This study uses bibliometric analysis and knowledge mapping methods to systematically explore the emerging research frontiers and development trajectories of 3-dimensional (3D) printing technology in the application of brain surgery, and provides new clues and research directions for future research by exploring hotspots and new topics. A comprehensive literature search was conducted through the Scientific Citation Index Core Collection (WoSCC) database on March 21, 2025 to identify relevant articles and reviews published between January 2015 and December 2024 on the application of 3D printing technology in brain surgery. For data analysis and visualization, we used CiteSpace and VOSviewer software to conduct rigorous bibliometric analysis and build knowledge domain maps. The authors' analysis covered 2982 papers contributed by 5550 authors from 1174 institutions in 90 regions, published in 236 journals. The authors have observed a steady increase in the number of publications annually, with Europe, Asia, North America, and Oceania leading the way in research output. The United States is in a leading position in research in this field. The University of London became the leading research institution in this field. The Journal of Craniofacial Surgery has made significant contributions to this field, with Tillinger Florian M being the most published and cited author. The most influential research hotspots focus on virtual surgical planning, tissue engineering, 3D printing and finite element analysis. The latest hotspots and research frontiers include 3D printing, augmented reality and reconstructive surgery. 3D printing technology has made significant progress in the field of brain surgery and has become a research frontier for continuous development in the field of medical innovation.

Large chest wall defects, as a result of wide local excision of chest wall pathology, require skeletal and soft-tissue reconstruction to restore the anatomical shape, structure, and respiratory function of the thorax. Reconstruction is challenging and requires the surgical reconstructive team to understand the anatomic and physiologic morbidity related to the defect and the choice of reconstructive techniques available to restore form and function. Rapidly emerging 3-dimensional (3D) printing technology allows the reconstructive surgical team to customize the therapeutic process of skeletal reconstruction by accurately mimicking the shape and structure of the chest wall being replaced. An integrated knowledge of the anatomy, physiology, mechanics of breathing, and respiratory tests is important to restore form and function. The focus of this article is to review the anatomy, physiology, and assessment of respiratory function from the classical textbooks and integrate this knowledge with the precise anatomy of the chest wall created by 3D printing technology. By doing so, this article will demonstrate how 3D printing may help the reconstructive team to understand the anatomic and physiologic morbidity related to the chest wall defect and the importance of taking each of these aspects into consideration when undertaking chest wall reconstruction of the thorax.

Three-dimensional (3D) printing is a promising technology that can use a patient's image data to create complex and personalized constructs precisely. It has made great progress over the past few decades and has been widely used in medicine including medical modeling, surgical planning, medical education and training, prosthesis and implants. Three-dimensional (3D) bioprinting is a powerful tool that has the potential to fabricate bioengineered constructs of the desired shape layer-by-layer using computer-aided deposition of living cells and biomaterials. Advances in 3D printed implants and future tissue-engineered constructs will bring great progress to the field of otolaryngology. By integrating 3D printing into tissue engineering and materials, it may be possible for otolaryngologists to implant 3D printed functional grafts into patients for reconstruction of a variety of tissue defects in the foreseeable future. In this review, we will introduce the current state of 3D printing technology and highlight the applications of 3D printed prosthesis and implants, 3D printing technology combined with tissue engineering and future directions of bioprinting in the field of otolaryngology.

The existing 3D printing methods exhibit certain fabrication-dependent limitations for printing curved constructs that are relevant for many tissues. Four-dimensional (4D) printing is an emerging technology that is expected to revolutionize the field of tissue engineering and regenerative medicine (TERM). 4D printing is based on 3D printing, featuring the introduction of time as the fourth dimension, in which there is a transition from a 3D printed scaffold to a new, distinct, and stable state, upon the application of one or more stimuli. Here, we present an overview of the current developments of the 4D printing technology for TERM, with a focus on approaches to achieve temporal changes of the shape of the printed constructs that would enable biofabrication of highly complex structures. To this aim, the printing methods, types of stimuli, shape-shifting mechanisms, and cell-incorporation strategies are critically reviewed. Furthermore, the challenges of this very recent biofabrication technology as well as the future research directions are discussed. Our findings show that the most common printing methods so far are stereolithography (SLA) and extrusion bioprinting, followed by fused deposition modelling, while the shape-shifting mechanisms used for TERM applications are shape-memory and differential swelling for 4D printing and 4D bioprinting, respectively. For shape-memory mechanism, there is a high prevalence of synthetic materials, such as polylactic acid (PLA), poly(glycerol dodecanoate) acrylate (PGDA), or polyurethanes. On the other hand, different acrylate combinations of alginate, hyaluronan, or gelatin have been used for differential swelling-based 4D transformations. TERM applications include bone, vascular, and cardiac tissues as the main target of the 4D (bio)printing technology. The field has great potential for further development by considering the combination of multiple stimuli, the use of a wider range of 4D techniques, and the implementation of computational-assisted strategies.

Green 3D bioprinting of plant cells: A new scope for 3D bioprinting