AI and Machine Learning enabling quality controlled bioprinting

Bioprinting technologies (including extrusion-based, inkjet, and light assisted) have been extensively studied in literature to fabricate three-dimensional constructs for Tissue Engineering applications, including implantation, in vitro modelling, drug screening, and cosmetics.

January 2025, 12th edition, The Netherlands

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However, currently very few examples are available on clinical trials using bioprinted products, and the ones that are actually available are still at a recruiting stage. This severe lack of clinical translation can be reconducted mainly 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. Machine learning algorithms have been recently proposed as a promising solution for the automatization and standardization of the quality assessment procedure, reducing the inter-batch variability and thus accelerating the bioprinted product clinical translation and commercialization.

This presentation will be focused on the main and most recent solutions on quality control and quality control enabled by machine learning that are being developed at different stages of the bioprinting process. Emphasis will be given on current challenges and future research directions related to using these technologies to enhance the quality assessment in bioprinting.

A presentation by Amedeo Bonatti, Post-Doc researcher at Dpt. of Information Engineering and Research Center E. Piaggio, University of Pisa, Italy.

About Amedeo Bonatti
He is a post-doc researcher at the Department of Information Engineering, University of Pisa, Italy. He holds a M.Sc. in Biomedical Engineering as well as a PhD in Information Engineering (under the supervision of Prof. Giovanni Vozzi and Prof. Carmelo De Maria), both at the University of Pisa. During the PhD, he focused on the development of total quality control strategies for different bioprinting processes, as well as their application in bone tissue engineering. The research was carried out in the framework of the H2020 Project GIOTTO (GA No 814410).
He also spent 5 months at the Singapore University of Technology and Design (SUTD) in Singapore under the supervision of Prof. Chua Chee Kai, to develop Deep Learning algorithms for quality control in extrusion-based bioprinting. Continuing the activities of the PhD, his current research is focused on the application of Artificial Intelligence and Machine Learning algorithms for quality control and automatic literature analysis in Bioprinting.

About Dpt. of Information Engineering and Research Center E. Piaggio, University of Pisa, Italy
The Biofabrication Group at the Research Center E. Piaggio (University of Pisa) aims at combining advanced fabrication technologies, including 3D printing, to create scaffolds, in-vitro models, biosensors and actuators using smart and (bio)materials. Applications are mainly focused on Tissue Engineering, regenerative medicine and biotechnologies.

For more information visit the website.

Amedeo Bonatti will speak at the 2023 edition of the 3D Medical Conference.