Image analysis as PAT-Tool for use in extrusion-based bioprinting

Strauß, Svenja; Meutelet, Rafaela; Radosevic, Luka; Gretzinger, Sarah; Hubbuch, Jürgen

doi:10.1016/j.bprint.2020.e00112

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…temperature, humidity), and measuring features of the construct during printing, like strand diameter, spacing and pore morphology in EBB, or droplet size and satellite droplet formation in IJB [49][50][51]. For example, a camera may be mounted on the bioprinter to capture the printed line from a top view and extract the line width in real-time by binarization and segmentation [52]. Researchers have also investigated the use of a flow sensor for in-process QC of pneumatic EBB.…”

Section: Qc Procedures For Bioprintingmentioning

confidence: 99%

Enhancing quality control in bioprinting through machine learning

Bonatti,

Vozzi,

De Maria

2024

Biofabrication

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Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for Tissue Engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e., difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e., no clear indication on the product classification in the current regulatory framework). In particular, quality control solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on quality control enabled by machine learning, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms and the performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

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Section: Qc Procedures For Bioprintingmentioning

confidence: 99%

Enhancing quality control in bioprinting through machine learning

Bonatti,

Vozzi,

De Maria

2024

Biofabrication

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“…The last method of hydrogel classification includes their implementation into the body: They might be non-injectable or injectable. Currently, many commercial injectable hydrogels serve as scaffolds for tissue engineering, e.g., EUFLEXXA ® , HyStem ® [89], Corning ® Matrigel ® [90], Biogelx™ [91] and others. The details are presented in Table 1.…”

Section: Hydrogels Dedicated To the Tissue Engineering Applicationsmentioning

confidence: 99%

Hydrogel, Electrospun and Composite Materials for Bone/Cartilage and Neural Tissue Engineering

2021

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Injuries of the bone/cartilage and central nervous system are still a serious socio-economic problem. They are an effect of diversified, difficult-to-access tissue structures as well as complex regeneration mechanisms. Currently, commercially available materials partially solve this problem, but they do not fulfill all of the bone/cartilage and neural tissue engineering requirements such as mechanical properties, biochemical cues or adequate biodegradation. There are still many things to do to provide complete restoration of injured tissues. Recent reports in bone/cartilage and neural tissue engineering give high hopes in designing scaffolds for complete tissue regeneration. This review thoroughly discusses the advantages and disadvantages of currently available commercial scaffolds and sheds new light on the designing of novel polymeric scaffolds composed of hydrogels, electrospun nanofibers, or hydrogels loaded with nano-additives.

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“…[ 9–11 ] Similar process optimization strategies include the preceding image‐analysis of larger datasets of printed objects to define the acceptable parameter range. Among the important parameters are thickness and uniformity, [ 12,13 ] the structure fidelity of extruded filaments while traversing lower orthogonal layers [ 14 ] and the connectivity respectively the separation of channels or pore systems. [ 15 ]…”

Section: Introductionmentioning

confidence: 99%

“…Among the important parameters are thickness and uniformity, [12,13] the structure fidelity of extruded filaments while traversing lower orthogonal layers [14] and the connectivity respectively the separation of channels or pore systems. [15] The second control strategy is to ensure the quality of the individual 3D printed object in a nondestructive manner.…”

mentioning

confidence: 99%

Magnetic resonance imaging as a tool for quality control in extrusion‐based bioprinting

Schmieg

Gretzinger

Schuhmann

et al. 2022

Biotechnology Journal

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Bioprinting is gaining importance for the manufacturing of tailor‐made hydrogel scaffolds in tissue engineering, pharmaceutical research and cell therapy. However, structure fidelity and geometric deviations of printed objects heavily influence mass transport and process reproducibility. Fast, three‐dimensional and nondestructive quality control methods will be decisive for the approval in larger studies or industry. Magnetic resonance imaging (MRI) meets these requirements for characterizing heterogeneous soft materials with different properties. Complementary to the idea of decentralized 3D printing, magnetic resonance tomography is common in medicine, and image data processing tools can be transferred system‐independently. In this study, a MRI measurement and image analysis protocol was evaluated to jointly assess the reproducibility of three different hydrogels and a reference material. Critical parameters for object quality, namely porosity, hole areas and deviations along the height of the scaffolds are discussed. Geometric deviations could be correlated to specific process parameters, anomalies of the ink or changes of ambient conditions. This strategy allows the systematic investigation of complex 3D objects as well as an implementation as a process control tool. Combined with the monitoring of metadata this approach might pave the way for future industrial applications of 3D printing in the field of biopharmaceutics.

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Image analysis as PAT-Tool for use in extrusion-based bioprinting

Cited by 11 publications

References 40 publications

Enhancing quality control in bioprinting through machine learning

Enhancing quality control in bioprinting through machine learning

Hydrogel, Electrospun and Composite Materials for Bone/Cartilage and Neural Tissue Engineering

Magnetic resonance imaging as a tool for quality control in extrusion‐based bioprinting

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