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UTERIAM: AI and bio-manufacturing to advance health

UTERIAM: AI and bio-manufacturing to advance health

Understanding how human tissues function without resorting to animal models or invasive techniques is one of the great challenges of biomedicine today. In response to this need comes UTERIAM, a project that combines artificial intelligence, 3D bioprinting and advanced bioinks to create uterine tissue cultures that accurately replicate the human physiological environment.

Thanks to an innovative approach, combining advanced technologies and AI algorithms capable of learning from large volumes of biomedical data, the project lays the foundations for a more personalised and less invasive medicine in gynaecological oncology, improving diagnosis, prevention and therapeutic response in endometrial cancer.

The goal: to optimise materials, manufacturing processes and cell validation in a single, reproducible platform that accelerates cancer research and the development of patient-tailored therapies.

From data to living tissue

UTERIAM's differential value lies in its ability to transform experimental data into automated decisions that optimise materials, designs and processes in bioprinting and electrospinning. The AI tool developed by Izertis analyses multidimensional information to predict optimal combinations of materials and geometries that generate 3D structures faithful to the native tissue.

Thanks to machine vision models, what previously required hours can now be done in seconds

Until now, much of this assessment has been done manually, a slow process subject to interpretation and with a higher risk of error. 

With UTERIAM, this approach is replaced by an automated system that extracts precise metrics, eliminates subjectivity and homogenises criteria at all stages of the process.

The solution is fed by both tabular data and microscopy images. Using machine vision models, it counts cells, measures their size, determines their condition and analyses their distribution in the three-dimensional culture. Thus, the best experimental conditions are identified in seconds, as opposed to the hours required by the traditional method.

A consortium specialising in health and advanced manufacturing

UTERIAM is the result of collaboration between Izertis, IDONIAL and SERIDA, covering the entire value chain: from digital technologies to biological validation. Izertis leads the development of the AI tool and data architecture; IDONIAL brings its expertise in 3D printing, bioprinting and bioinks; and SERIDA contributes with its knowledge in 3D models of bovine endometrium and animal reproduction.

These projects are special for the team, as they represent the beginnings of Izertis

"UTERIAM is already the fourth project in which Izertis participates involving cell imaging and cell culture. These projects are special for the team, as they represent the beginnings of Izertis in this line of work," says Samuel Camba, project technician.

The combination of capabilities, together with the experience gained, allows complex challenges to be addressed, from defining material and process requirements to creating models that integrate different endometrial and tumour cell types.

Impact on cancer research and the health sector

UTERIAM is aligned with the objectives of active and healthy ageing and with the strategic lines in new advanced therapies and artificial intelligence defined in the RIS3 programme or Smart Specialisation Strategies. The project aims to develop a future software-biomedical prototype that will position Izertis at the forefront of applying AI and data to in vitro tissue design for oncology.

In addition to its scientific impact, the initiative reinforces the positioning of Izertis in the field of digital health and biomedicine, generates differential knowledge in 3D bioprinting and opens new avenues for the development of AI solutions for cancer research.

This project has been co-funded by the Government of the Principality of Asturias through the SEKUENS 2024 call for R&D projects and by the European Union through the ERDF (file number IDE/2024/000469).

 


 

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