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or how they produce their desired outputs. This lack of transparency makes these algorithms unapproachable for clinical applications, which require deep trust in all aspects of the system. Furthermore, there are legal, ethical, and regulatory rules that need to be followed for deep learning detection systems to be used in clinical set­tings [45–48]. As a result, there is an emerging need for interpretable DL.These methods allow end users to evaluate the usefulness and errors of ML/DL predictions and diagnoses before taking action based on the advice [49].
P. Motie et al.
3.2 Generalizability
An algorithm can only have that high performance when tested with the fed data. If you feed it new data, it will most likely not do as well as the training. In other words, it is unrealistic to expect an algorithm designed for one set of questions to be helpful for another set of questions that were not covered by the original research [50]. On the other hand, most of the studies we’ve come across so far have been retrospective and used previous data to train and test the newly designed algorithm, so we can’t be sure that the model would be practical when facing the new emerging cases. Although reported accuracy may seem enough to judge the efcacy of AI models, their clinical applicability remains unclear [4].
3.3 Algorithmic Bias
The ndings show that there are three major sources of algorithmic bias: data bias, method bias, and societal bias. For instance, a training dataset may not be adequate or represent a random sample from the target population, thus resulting in either sample inadequacy or sample selection bias [51].
3.4 Adaptation
This point refers to the amount of effort surgeons need to learn to work with a new system or technology. As previously described, AI models are limited in the sense that they don’t explain how their decisions were made. Essentially, these models are considered black boxes, and there’s a high risk that they’ll become even more obscure as they get better [4]. Adaptation to these newly developed AI-based sys­tems might be time-consuming, especially for the previous generation that may have gotten used to specic tools. However, new generations of dentists might be considered digital natives who will naturally use digital tools.
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3.5 Ethical Issues
It is crucial to address the ethical concerns surrounding the use of AI, as it has the potential to compromise privacy and security, violate condentiality and informed consent, and undermine the independence of patients. AI in health care must be more ethical; otherwise, it could lead to distrust among the public. Precision medi­cine is a sensitive topic due to the revelation of critical information such as patient’s genomic data, drug intake, health status, metabolic status, and so on [52]; therefore, it is of utmost importance to make sure that information security legislation and policies are put into place to protect individuals, especially patients [14]. Using patients’ medical records and data as part of an algorithm is another ethical concern. In some cases, these datasets might be used to calibrate algorithms for commercial­ization, for which patients will need to pay again if they use them. However, despite their importance, these indirect ethical dilemmas were not taken into account in previous studies. To reach the sufcient data diversity for training a model with accurate outcomes, AI should be developed in a way that is compatible with con­serving social-cultural diversity and gender diversity without restricting lifestyle choices or personal experiences [53].
AI can also be implemented in other parts of medical care, like devices and robots (also called care bots) [54]. Surgeons have been increasingly relying on robotic-assisted minimally invasive surgery in recent years. Thus, Larson etal. con­structed principles that are based on a just culture1 to support an ethical framework to minimize preventable harm for surgeons embracing new technology [55]. In the same manner, Heyen and colleagues used the ve structural features of profession­alized medicine as an analytical lens, to see how AI impacts the medical profession [56]. In these mentioned principles, the surgeon interacts with the patient directly and the AI is just a tool to help with the decision-making process. Responsibility and the patient-clinician relationship are two critical considerations, so developing and using AI should not be interpreted as diminishing human responsibility when making decisions [53]. In addition, with the new revolution of robotic surgeons doing all the tasks without the need for human presence, there should be new laws concerning the ongoing debate about who should be responsible for the actions that autonomous robots commit, keeping in mind that robots have no understanding of blame, sanctions, accountability, liability, or culpability [57].
3.6 Cybersecurity Issues
There are also an increasing number of cybersecurity risks [57]. For example, Bonaci etal. recently showed that there are cybersecurity risks in surgical robots [58]. In order to prevent cyberattack, we should take steps to ensure that no software is altered during surgery.
1
A model of workplace justice is designed to create fairness for providers and better outcomes for
patients. Just culture puts the emphasis from the error to the system design and behavioral choices.
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P. Motie et al.
3.7 Patients’ Rights andAutonomy
To get the best results from the treatment plan, the surgeon must consider the patients’ biographical or demographic information. Issues like the patient’s person­ality, life situation, or cultural background can all affect the output. It’s crucial more than ever to consider patients’ intentions while using AI tools in clinical practice. As AI algorithms cannot take into account patient autonomy, it is essential for medical practitioners to reect on their patient’s concerns carefully [56].
References
1. Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M.Articial intelligence and the future of surgical robotics. Ann Surg. 2019;270(2):223–6.
2. Han S.The Fourth Industrial Revolution and oral and maxillofacial surgery. J Korean Assoc Oral Maxillofac Surg. 2018;44(5):205–6.
3. Schwendicke F, Samek W, Krois J.Articial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99(7):769–74.
4. Rekawek P, Rajapakse CS, Panchal N.Articial intelligence: the future of maxillofacial prog­nosis and diagnosis? J Oral Maxillofac Surg. 2021;79(7):1396–7.
5. Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, etal. Deep neural net­works for dental implant system classication. Biomolecules. 2020;10(7):984.
6. Ossowska A, Kusiak A, Świetlik D.Articial intelligence in dentistry—narrative review. Int J Environ Res Public Health. 2022;19(6):3449.
7. Lu C-H, Ko EW-C, Liu L.Improving the video imaging prediction of postsurgical facial pro­les with an articial neural network. J Dent Sci. 2009;4(3):118–29.
8. Corbella S, Srinivas S, Cabitza F.Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;132(2):225–38.
9. Maruyama T, Hayashi N, Sato Y, Hyuga S, Wakayama Y, Watanabe H, etal. Comparison of medical image classication accuracy among three machine learning methods. J Xray Sci Technol. 2018;26(6):885–93.
10. Chen Y-W, Stanley K, Att W.Articial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248–57.
11. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R.Use of articial intelligence in den­tistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87(C):l7.
12. Jain K, Shah V. Articial intelligence for precision medicine and better healthcare. Primary Health Care Open Access. 2020;10:1–4.
13. Uddin M, Wang Y, Woodbury-Smith M.Articial intelligence for precision medicine in neu­rodevelopmental disorders. NPJ Digit Med. 2019;2:112.
14. Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, etal. Precision medicine in the era of articial intelligence: implications in chronic disease management. J Transl Med. 2020;18(1):1–12.
15. Polverini PJ, D’Silva NJ, Lei YL.Precision therapy of head and neck squamous cell carci­noma. J Dent Res. 2018;97(6):614–21.
16. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T.Articial intelligence in precision car­diovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–64.
17. Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, etal. Translating cancer genomics into precision medicine with articial intelligence: applications, challenges and future per­spectives. Hum Genet. 2019;138(2):109–24.
Future Trends ofUsing Articial Intelligence inOral andMaxillofacial Surgery
https://t.me/medicina_free
18. Kim YJ, Kelley BP, Nasser JS, Chung KC.Implementing precision medicine and articial intelligence in plastic surgery: concepts and future prospects. Plast Reconstr Surg Glob Open. 2019;7(3):e2113.
19. van de Lande LS, Papaioannou A, Dunaway DJ.Geometric morphometrics aided by machine learning in craniofacial surgery. J Orthod. 2019;46(1_Suppl):81–3.
20. Mackay BS, Marshall K, Grant-Jacob JA, Kanczler J, Eason RW, Oreffo RO, etal. The future of bone regeneration: integrating AI into tissue engineering. Biomed Phys Eng Express. 2021;7(5):052002.
21. Entekhabi E, Nazarpak MH, Sedighi M, Kazemzadeh A.Predicting degradation rate of genipin cross-linked gelatin scaffolds with machine learning. Mater Sci Eng C. 2020;107:110362.
22. Ghosh R, Chanda S, Chakraborty D.Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework. Med Eng Phys. 2021;95:64–75.
23. Wu C, Entezari A, Zheng K, Fang J, Zreiqat H, Steven GP, et al. A machine learning-based multiscale model to predict bone formation in scaffolds. Nat Comput Sci. 2021;1(8):532–41.
24. Mahmood M, Visan A, Ristoscu C, Mihailescu I.Articial neural network algorithms for 3D printing. Materials. 2021;14:163.
25. Zhang W, Liu J, Shan H, Yin F, Zhong B, Zhang C, etal. Machine learning-guided evolution of BMP-2 knuckle epitope-derived osteogenic peptides to target BMP receptor II.J Drug Target. 2020;28(7-8):802–10.
26. Gardner JM, Hunt KA, Ebel AB, Rose ES, Zylich SC, Jensen BD, etal. Machines as crafts­men: localized parameter setting optimization for fused lament fabrication 3D printing. Adv Mater Technol. 2019;4(3):1–10.
27. Jin Z, Zhang Z, Gu GX.Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning. Manuf Lett. 2019;22:11–5.
28. Herriott C, Spear AD.Predicting microstructure-dependent mechanical properties in addi­tively manufactured metals with machine- and deep-learning methods. Comput Mater Sci. 2020;175:109599.
29. Gu GX, Chen CT, Richmond DJ, Buehler MJ.Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horiz. 2018;5(5):939–45.
30. Shevchik SA, Kenel C, Leinenbach C, Wasmer K.Acoustic emission for in situ quality moni­toring in additive manufacturing using spectral convolutional neural networks. Addit Manuf. 2018;21:598–604.
31. Ye D, Hsi Fuh JY, Zhang Y, Hong GS, Zhu K.In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans. 2018;81:96–104.
32. Jafari-Marandi R, Khanzadeh M, Tian W, Smith B, Bian L.From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based addi­tive manufacturing. J Manuf Syst. 2019;51:29–41.
33. Yu C, Jiang J.A perspective on using machine learning in 3D bioprinting. Int J Bioprinting. 2020;6(1):4–11.
34. Kim J, McKee JA, Fontenot JJ, Jung JP.Engineering tissue fabrication with machine intel­ligence: generating a blueprint for regeneration. Front Bioeng Biotechnol. 2020;7:1–9.
35. An J, Chua CK, Mironov V. Application of machine learning in 3D bioprinting: focus on development of big data and digital twin. Int J Bioprinting. 2021;7(1):1–6.
36. Jin Z, Zhang Z, Shao X, Gu GX. Monitoring Anomalies in 3D Bioprinting with Deep Neural Networks. ACS Biomater Sci Eng. 2021. https://doi.org/10.1021/acsbiomaterials.0c01761. Epub ahead of print. PMID: 33882674.
37. Ruberu K, Senadeera M, Rana S, Gupta S, Chung J, Yue Z, etal. Coupling machine learn­ing with 3D bioprinting to fast track optimisation of extrusion printing. Appl Mater Today. 2021;22:100914.
38. Ji Q, Chen M, Wang XV, Wang L, Feng L.Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning. Robot Comput Integr Manuf. 2022;73:102209.
343
344
https://t.me/medicina_free
39. Gumbs AA, Frigerio I, Spolverato G, Croner R, Illanes A, Chouillard E, etal. Articial intel­ligence surgery: how do we get to autonomous actions in surgery? Sensors. 2021;21(16):1–18.
40. Andras I, Mazzone E, van Leeuwen FWB, De Naeyer G, van Oosterom MN, Beato S, etal. Articial intelligence and robotics: a combination that is changing the operating room. World J Urol. 2020;38(10):2359–66.
41. Bassyouni Z, Elhajj IH.Augmented reality meets articial intelligence in robotics: a system­atic review. Front Robot AI. 2021;8:1–20.
42. Ma Q, Kobayashi E, Wang J, Hara K, Suenaga H, Sakuma I, etal. Development and prelimi­nary evaluation of an autonomous surgical system for oral and maxillofacial surgery. Int J Med Robot Comput Assist Surg. 2019;15(4):e1997.
43. Kwon IJ, Kim SM, Hwang SJ.Development of autonomous robot osteotomy for mandibular ramal bone harvest and evaluation of its accuracy: a phantom mandible-based trial. Appl Sci. 2021;11(6):2885.
44. Hagendorff T, Wezel K. 15 Challenges for AI: or what AI (currently) can’t do. AI Soc. 2020;35(2):355–65.
45. Angelov P, Soares E. Towards explainable deep neural networks (xDNN). Neural Netw. 2020;130:185–94.
46. Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, etal. An explainable deep­learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng. 2019;3(3):173–82.
47. Gulum MA, Trombley CM, Kantardzic M.A review of explainable deep learning cancer detection models in medical imaging. Appl Sci. 2021;11(10):4573.
48. Brunese L, Mercaldo F, Reginelli A, Santone A.Explainable deep learning for pulmonary dis­ease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed. 2020;196:105608.
49. Jin D, Sergeeva E, Weng WH, Chauhan G, Szolovits P.Explainable deep learning in health­care: a methodological survey from an attribution view. WIREs Mech Dis. 2022;14(3):e1548.
50. Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S.Dentronics: towards robotics and articial intelligence in dentistry. Dent Mater. 2020;36(6):765–78.
51. Akter S, McCarthy G, Sajib S, Michael K, Dwivedi YK, D’Ambra J, etal. Algorithmic bias in data-driven innovation in the age of AI.Amsterdam: Elsevier; 2021. p.102387.
52. Azencott C-A. Machine learning and genomics: precision medicine versus patient privacy. Philos Trans R Soc A Math Phys Eng Sci. 2018;376(2128):20170350.
53. Mörch C, Atsu S, Cai W, Li X, Madathil S, Liu X, etal. Articial intelligence and ethics in dentistry: a scoping review. J Dent Res. 2021;100(13):1452–60.
54. Hamet P, Tremblay J.Articial intelligence in medicine. Metabolism. 2017;69s:S36–40.
55. Larson JA, Johnson MH, Bhayani SB.Application of surgical safety standards to robotic sur­gery: ve principles of ethics for nonmalecence. J Am Coll Surg. 2014;218(2):290–3.
56. Heyen NB, Salloch S.The ethics of machine learning-based clinical decision support: an anal­ysis through the lens of professionalisation theory. BMC Med Ethics. 2021;22(1):1–9.
57. O’Sullivan S, Nevejans N, Allen C, Blyth A, Leonard S, Pagallo U, etal. Legal, regulatory, and ethical frameworks for development of standards in articial intelligence (AI) and autonomous robotic surgery. Int J Med Robot Comput Assist Surg. 2019;15(1):1–12.
58. Bonaci T, Herron J, Yusuf T, Yan J, Kohno T, Chizeck HJ. To make a robot secure: an experi­mental analysis of cyber security threats against teleoperated surgical robots. arXiv preprint. 2015; https://arxiv.org/abs/1504.04339v2.
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Application ofBioprinting Technology
https://t.me/medicina_free
inOral andMaxillofacial Surgery
SadraMohaghegh andHaniehNokhbatolfoghahaei
1 Introduction
Additive manufacturing is one recently emerged technology used to fabricate pre­designed porous scaffolds for maxillofacial bone regeneration [1, 2]. The aim is to fabricate products that can mimic the macro and microstructural features of the extracellular matrix [3–7]. In the conventional approach, cells are seeded on the scaffolds after the printing phase to protect them from the thermal and chemical stresses of the fabrication procedure [8]. However, this procedure does not provide scaffolds with homogenized cell distribution, especially in depth [9]. Besides, a limited number of cells can be seeded on the scaffolds considering their size. Therefore, bioprinting technology has emerged to overcome the mentioned issues.
Bioprinting refers to additive manufacturing technology in which the scaffold is fabricated from a blend of cells and biocompatible materials in a layer-by-layer manner [10, 11]. The mentioned blend is called bio-ink [10]. Considering the bio­compatibility of the printing procedure compared to traditional printing, biological agents such as growth factors can be safely printed with the scaffold. Commonly used bio-inks are made of synthetic and natural hydrogels such as gelatin, collagen, alginate, chitosan, and tissue-specic decellularized extracellular matrix due to their inherent capacity of providing a compatible, aqueous environment for cellular activity [12].
Three main technologies have been reported for bioprinting of the scaffolds [1,
13] (Fig.1). In the extrusion-based method, the struts are formed from the material
S. Mohaghegh Student Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
H. Nokhbatolfoghahaei (*) Dental Research Center, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. Khojasteh et al. (eds.), Emerging Technologies in Oral and Maxillofacial Surgery, https://doi.org/10.1007/978-981-19-8602-4_17
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Inkjet Bioprinting Laser-assisted Bioprinting Extrusion Bioprinting
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S. Mohaghegh and H. Nokhbatolfoghahaei
Thermal
Heater
Fig. 1 Categorization of the commonly used bioprinting technologies [13]
Vapor Bubble
Piezoelectric
Actuator
Laser Pulse
Energy-absorbing Layer
Pneumatic / Piston / Screw
Donor Layer
Bioink
extruded from a nozzle. The curable bio-ink is used to fabricate the scaffold laser­based method. Inkjet bioprinting refers to creating and fusing cell-laden droplets to form the scaffold. Details of each technology and its advantages and disadvantages are discussed in the current chapter.
2 Extrusion-Based Approach
In this approach, the bio-ink is extruded from a nozzle through pressure, piston, and rotating screws. Screws are usually used in the case of printing high-viscosity mate­rials [14]. However, screw-based extrusion can jeopardize cell viability signicantly [15]. Although applying pressure is the most common approach, delay in the mate­rial extrusion in this method can decrease the printing resolution.
The material is extruded on a platform which can be stasis or have pre-dened motion. The platform temperature is a signicant criterion that must be optimized in the printing procedure [9]. For instance, considering that the stability of the un­crosslinked hydrogels is related to temperature, the nozzle temperature is set higher to let material extrusion. In contrast, platform temperature is set to lower degrees to cause material xation and strut formation [16].
Besides, the bed must provide adequate conditions for cell growth. Therefore, Petri dishes and glass slides are suitable options for bed. More recently, direct tissue bioprinting on the wound has been reported in patients suffering from burn wounds. Indeed, the defect is considered as the bed in this instance [17].
Thermally induced gelation and cross-linkability are two main features of mate­rials used for the extrusion bioprinting procedure [10, 18]. Hydrogels are the most commonly used materials in this case. They have high water contact and lower mechanical stability than polymers commonly used in the conventional printing procedure. Therefore, pure hydrogels cannot be used for printing, and some modi­cations are inevitable to optimize the material [18].
2.1 Optimizing Printing Materials
The rst approach to optimize the printing material is to improve mechanical fea­tures by adding more stable structures such as mineral materials (e.g.,
Application ofBioprinting Technology inOral andMaxillofacial Surgery
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hydroxyapatite (HA) [19], tricalcium phosphate (TCP) [6, 8, 20], and Bioglass (BG) [21, 22]). Studies showed signicant improvements in the mechanical and biological behavior of the ceramic-reinforced bio-printed hydrogels [8, 19, 20]. However, adding high amounts of ceramics can adversely impact the chemical bonding of the hydrogel parts [23]. Besides, mineral particles can cause nozzle plugging, and they may not homogenously distribute in the blend. Alongside the mineral materials, more stable hydrogels such as alginate can be used to improve the scaffold’s stability. More specically, alginate has been widely used combined with gelatin [24, 25]. However, its main drawback is its slow invivo degradation due to lack of alginate-degrading enzymes in the mammalian cells [26]. Besides, they cannot provide the optimal surface for cell adhesion. Subsequently, alginate is not a proper material to be used as the main component of the scaffold, and it has been used as an additional part. Printing bio-ink in the liquid platform is another method to increase the stability of the scaffolds during the fabrication [16, 27].
Chemical modication of the printing materials is the second approach to over­come the drawback of the pure hydrogels. Adding a methacrylate group is one of the recommended methods. Gelatin methacrylate (GelMA) and hyaluronic acid meth­acrylate (MeHA) are the commonly used materials in this concern [28]. Additional parts can also be chemically modied to improve the function of the scaffold. Better chemical bonding, higher biocompatibility, and enhanced mechanical properties have been reported for oxidized alginate, called alginate dialdehyde (ADA) [29–
32]. It has to be mentioned that alginate can be irradiated to provide improved bio-
logical behavior [33].
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2.2 Cell Viability Improvements
Implanting scaffolds with higher cell density in the defect is one of the main advan­tages of bioprinting compared to the traditional approach. Besides, homogenous cell distribution of bioprinting improves the scaffolds’ biological function speci­cally in the marginal areas [34]. However, cells are exposed to further stress in the bioprinting procedure, which necessitates customizing the printing set-up to increase cell viability (Fig.2).
2.2.1 Printing Pressure andNozzle Geometry
It is apparent that increasing the pressure adversely affects cell viability [35]. Besides, the cell may survive in some instances while its function or phenotype is altered [36]. Alongside the pressure, nozzle features can also impact cell viability. The lower nozzle diameter and higher nozzle length can decrease cell viability due to higher applied shear stress to the cells [37]. However, the nozzle width can­not be over-increased since it can impair the printing resolution. Nozzle shape can also impact the applied shear stress to the cells. Choosing the optimal nozzle shape is related to the printing pressure. Indeed, higher cell viability was reported in conical- shaped nozzles in low-pressure printing (i.e., <300kPa), while there was no difference between the conical and cylindrical nozzles in higher pres­sures [38].
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S. Mohaghegh and H. Nokhbatolfoghahaei
Cell Viability in extrusion bioprinting
Fig. 2 Factors that can impact the cell viability in the extrusion bioprinting procedure
Printing Pressure
Nozzle Diameter
Nozzle Shape
Nozzle Width
Fabrication Time
Extrusion Speed
Material Characteristics
2.2.2 Fabrication Time, Extrusion Speed,
andMaterial Characteristics
Cations must be taken to design the scaffolds in a manner that they can be printed in a short time. Indeed, cells cannot tolerate the printing conditions for a long time [12]. One way is to increase the printing speed. It has to be considered that printing speed denes the imposed shear/compression stress to the cells, and it cannot be increased carelessly. Decreasing the speed leads to material accumulation, which increases the applied compression force to the cells [39]. On the other hand, higher speeds lead to material stretching that increases shear stress to the cells [39]. Last but not least, material features can impact cellular viability. Insufcient cellular density and high blend viscosity increase the applied shear stress to the cells and decrease their viability [37].
2.3 Crosslinking
Three main factors have to be considered in the crosslinking procedure [40]. First, the material is used to crosslink the hydrogel. Indeed, a single type of hydrogel may be crosslinked with various materials, leading to fabricating scaffolds with the same material and different biological and physicochemical properties [28]. Second, the time required to expose the material to the crosslinking agent is another factor. On one side, the crosslinking procedure is performed more completely in the case of exposing scaffolds to the agent for a longer time. However, this prolonged time can adversely impact cell viability. Therefore, contrary to conventional printing, only biocompatible agents can be applied as the crosslinking agent in bioprinting.
Third, the step in which crosslinking is performed is crucial. This procedure is commonly performed after the bioprinting phase to increase its stability. In the other approach, the material is crosslinked as it is extruded, as seen in the coaxial bio­printing [41]. This procedure is mainly used for vascular bioprinting in which the crosslinking agent is extruded from the nozzle in the vessel’s core and the printing material is extruded for the circular-shaped nozzle that surrounds the core nozzle. Therefore, the tube-shaped struts are printed [42]. Preprinting crosslinking is the
Application ofBioprinting Technology inOral andMaxillofacial Surgery
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third approach that aims to increase the material stability during the printing proce­dure and decrease the concentration and duration of the post-fabrication crosslink­ing [6]. Over-crosslinking of the blend before printing can cause nozzle plugging.
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3 Laser-Based Stereolithography (SLA) Bioprinting
In this method, a photosensitive resin is polymerized in a layer-by-layer manner based on the illumination pattern. Higher accuracy and printing resolution are reported for SLA bioprinting than the extrusion-based method [43].
3.1 Material
Same as extrusion-based bioprinting, hydrogels are the chosen material for SLA bioprinting. However, they must be modied to be crosslinked with UV or visible light. Acryloyl or alkenyl functional groups are added to them for this purpose.
3.1.1 Photo-crosslinkable Polymer
Two groups of biomaterials are used in the SLA bioprinting [44]: First is photosen­sitive hydrogels with acryloyl groups such as poly(ethylene glycol) diacrylate (PEGDA), PEGDMA, GelMA, and dextran methacrylate (DexMA). However, pos­sible drawbacks of methacrylic groups on the cell surface may conne their applica­tion. Besides, demanding high amounts of free radicals for commencement of the reaction and prolonged irradiation time are the other drawbacks of using meth­acrylic polymers for SLA bioprinting [45, 46]. The second group is photo­crosslinkable gelatin which is the most commonly used biomaterial in SLA bioprinting due to its optimal biological features [47].
Fast crosslinking systems have emerged to shorten the irradiation phase and increase cell viability. Orthogonal UV crosslinking or thiol-ene chemistry is the most commonly used approach in this case. This method is based on a chemical reaction between thiol and alkene agents [48, 49]. Chemical products produced in this approach are less toxic than the traditional method, and fewer free radicals are required to start the reaction in this case [45, 50]. Applying the accelerated method to fabricate modied gelatin scaffolds has been shown [51].
3.1.2 Non-photo-crosslinkable Polymers+Photoinitiators
Photoinitiators produce free radicals that crosslink the photosensitive hydrogels [52]. Photoinitiator type denes the irradiation source (i.e., UV or visible light). Considering the chemical reaction type, two types of photoinitiators exist [44]. Type 1 initiators (e.g., Irgacure 2959 and LAP) are the simpler ones that have faster chemical reactions. LAP imitators can be activated through both UV and visible light, and it has better biocompatibility and polymerization rate than Irgacure 2959 [53]. Type 2 initiators create the free radical through the cascade of reactions that complicate and prolong the procedure.
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