Enhancing quality control in bioprinting through machine learning

Abstract

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.

A practical machine learning approach for predicting the quality of 3D (bio)printed scaffolds

“…ML is categorized into supervised learning, where models are trained on labeled data to make accurate predictions, and unsupervised learning, which uncovers hidden structures and patterns in unlabeled data [13]. Deep learning (DL) is a subset of ML involving neural networks with many hidden layers; 'deep' refers to these hidden layers [14,15].…”

“…AM encompasses several techniques, including Fused Deposition Modeling, inkjet bioprinting, light-assisted bioprinting, and extrusion-based bioprinting. Extrusionbased bioprinting is particularly prominent due to its versatility in handling diverse materials and ease of use [15,24]. Given the intricate challenge of replicating tissue, which involves numerous variables requiring precise adjustment, and the capacity of AI to discern complex relationships among various parameters, numerous researchers have explored diverse applications of AI within the 3D (bio)printing process [25].…”

“…Many researchers do not make their datasets publicly available, and a lack of data has hindered the proliferation of AI and data-driven research in this field. Based on Bonatti et al, only about a quarter of published papers on using ML in bioprinting have made their dataset publicly available [15].…”