Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids

Tröndle, Kevin; Miotto, Guilherme; Rizzo, Ludovica; Pichler, Roman; Koch, Fritz; Koltay, Peter; Zengerle, Roland; Lienkamp, Soeren S.; Kartmann, Sabrina; Zimmermann, Stefan

doi:10.18063/ijb.v8i2.528

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…The developed CNN model successfully categorized cell viability into three classes with a balanced accuracy of 78.7%. CNN has also demonstrated good generalization performance when predicting the cell viability of bioprinted renal spheroids under varied inhibitory concentrations as well as experimental settings [ 47 ] . This may be a new pathway to prompt efficient cell viability studies by segmenting cell/nuclei in fluorescence images and then counting live/dead cells using the developed DL models.…”

Section: ML Applications In Cell Performance Studiesmentioning

confidence: 99%

Machine learning and 3D bioprinting

Sun¹,

Yao²,

An³

et al. 2024

IJB

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With the growing number of biomaterials and printing technologies, bioprinting has brought about tremendous potential to fabricate biomimetic architectures or living tissue constructs. To make bioprinting and bioprinted constructs more powerful, machine learning (ML) is introduced to optimize the relevant processes, applied materials, and mechanical/biological performances. The objectives of this work were to collate, analyze, categorize, and summarize published articles and papers pertaining to ML applications in bioprinting and their impact on bioprinted constructs, as well as the directions of potential development. From the available references, both traditional ML and deep learning (DL) have been applied to optimize the printing process, structural parameters, material properties, and biological/ mechanical performance of bioprinted constructs. The former uses features extracted from image or numerical data as inputs in prediction model building, and the latter uses the image directly for segmentation or classification model building. All of these studies present advanced bioprinting with a stable and reliable printing process, desirable fiber/droplet diameter, and precise layer stacking, and also enhance the bioprinted constructs with better design and cell performance. The current challenges and outlooks in developing process–material–performance models are highlighted, which may pave the way for revolutionizing bioprinting technologies and bioprinted construct design.

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Section: ML Applications In Cell Performance Studiesmentioning

confidence: 99%

Machine learning and 3D bioprinting

Sun¹,

Yao²,

An³

et al. 2024

IJB

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“…Finally, as can be seen in table 2, ML has been studied to automatically extract relevant features related to the state of bioprinted cells from image data [104,109]. In an interesting application, Yao et al proposed an unsupervised algorithm based on generative adversarial networks (GANs) for the automatic segmentation of cell nuclei in 3D scaffolds (figure 6(a)).…”

Section: For Post-process Qcmentioning

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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“…The team demonstrated that the detection effect of the method was good, providing a new solution for the rapid screening of 3D-bioprinted organs. Tröndle et al [ 92 ] obtained a renal sphere by bioprinting and tested its toxicity based on deep learning technology. Due to the precise deposition of low- volume, low-viscosity bioink, the renal sphere was generated by drop-on-demand bioprinting.…”

Section: Defect Detection During 3d Bioprintingmentioning

confidence: 99%

Machine learning boosts three-dimensional bioprinting

Ning¹,

Zhou²,

Joo³

2023

IJB

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Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layer-by-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Data-driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

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Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids

Cited by 9 publications

References 22 publications

Machine learning and 3D bioprinting

Machine learning and 3D bioprinting

Enhancing quality control in bioprinting through machine learning

Machine learning boosts three-dimensional bioprinting

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