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International Journal of Bioprinting Medical regenerative in situ bioprinting

extrusion/inkjet-based bioprinting methodology for the systems. In the future, advancements are anticipated in
reconstruction of intricate craniomaxillofacial defects. remote-controlled robotic surgery, where doctors can
In this approach, they employed extrusion bioprinting to program surgical procedures based on patient data and
directly print an osteogenic hard tissue bioink, while inkjet remotely operate robots to perform repairs or treatment.
bioprinting was utilized for the deposition of a soft tissue
bioink with lower viscosity. Remarkably, their findings 2.2. Handheld in situ bioprinting system
demonstrated approximately 80% skin reconstruction 2.2.1. System setup
within 10 days and 50% bone regeneration after 6 weeks. Handheld in situ bioprinting systems (HISBS), also known
2.1.5. Challenges as hand devices, can be easily manipulated by operators
While RASBS has demonstrated promise for achieving without a professional background. Unlike RASBS,
complex in situ bioprinting, there are still some challenges the positioning and movement of the setup during the
that need to be addressed: printing process are typically controlled by operators.
HISBS is particularly suitable for minor wounds as it is
(i) The limited workspace and large setup volume make more flexible, can print structures of any shape, and can
it difficult to use for internal tissue repair. adjust the print path in real time. Furthermore, HISBS has
(ii) The process requires complex equipment and is time- a shorter print response time since it does not require prior
consuming, as it involves scanning, 3D geometry preparation, such as scanning wound shapes or calibrating
construction, printing path optimization, and error path errors. 55,87
compensation. In the actual in situ bioprinting process, an initial

(iii) Higher criteria for bioinks are required, as not all debridement step is often required, which can result in
materials are suitable for in situ bioprinting. New a mismatch between the prefabricated construct and the
materials that are compatible with this technique defect. To address this challenge, handheld bioprinters
must be developed, e.g., bioinks for printing on non- have been developed for in situ bioprinting applications.
planar wounds require a higher viscosity to match HISBS offers several advantages, including manual
the wound shape.
control of the printing position and speed, low-cost,
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(iv) For successful tissue regeneration, the printed tissue portability, lack of computer-aided requirements, ease
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must be able to integrate with the host tissue. of sterilization, and suitability for hard-to-reach and
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(v) Improving the automation and scalability of in situ non-flat wounds. Handheld printers can directly deposit
bioprinting systems can reduce the cost and increase biomaterials inside the defect to build a tissue scaffold.
the speed of tissue production. This effort may Handheld bioprinting does not require a high-definition
involve developing new software for designing and 3D scanner to scan the defect, unlike automated systems.
printing tissues, as well as creating systems capable Additionally, handheld devices can be easily operated
of printing multiple tissues simultaneously. without requiring specialized knowledge, enabling
operators to build constructs using hand movements and
In response to these challenges, artificial intelligence adjust the printing strategy in real-time.
(AI) technology has displayed great application potential
in the field of bioprinting. New biomaterials and structural 2.2.2. Performance optimization strategy of the
design can be developed through AI and machine printed structure
learning to be compatible with printing technologies and Portable bioprinters, also known as hand bioprinters,
application environments. Limon et al. established a have successfully fabricated various types of tissues,
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prediction model of key process parameters of extrusion- including skin, muscle, 53,54 cartilage, 55,56,57 bone, and
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based in situ 3D bioprinting using machine learning dental tissue, with most handheld bioprinting utilizing
method, and the accuracy of the model to predict the extrusion-based methods. Other bioprinting methods,
printing wire width was 85%. Qiao et al. used a machine such as droplet and laser-assisted, are challenged by nozzle
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learning model to predict the effect of cryoprotectant clogging and miniaturization. Russell et al. developed a
formulations on cryoprotected bioinks. Additionally, hand bioprinter (Figure 3A) to treat a murine volumetric
researchers could predict the number of cells in the printed muscle loss (VML) injury. The hand bioprinter directly
droplet through machine learning algorithms, achieving printed gelatin-based hydrogels, which were crosslinked in
real-time evaluation of the number of printed cells during situ under UV light. The results indicated that this device
the printing process. AI-mediated real-time monitoring could maintain the viability of muscle cells and promote
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and feedback systems can also improve the degree of cell proliferation. Using the same printing method, Quint
automation and printing accuracy of in situ bioprinting et al. presented a growth factor-eluting bioink to treat
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Volume 10 Issue 5 (2024) 53 doi: 10.36922/ijb.3366