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New Finnish consortium aims to enhance diagnostics and combat pandemics

Future of Diagnostics (FUDIS) launches to enhance the development of fast and precise diagnostic methods for a number of diseases and healthcare challenges.
Written by Aiforia

The COVID-19 pandemic has highlighted the importance of efficient diagnostic methods. Beyond battling pandemics, there are also significant diagnostic needs in everyday healthcare but healthcare systems around the world are struggling with rapidly increasing costs. The Future of Diagnostics (FUDIS) co-innovation consortium was created to address these challenges — to develop reliable, rapid, sensitive and cost-effective point-of-care diagnostic methods.

Formed from several Finnish research organizations and technology companies working in the field of in vitro diagnostics, FUDIS aims to develop new tools for point-of-care diagnostics for a multitude of disease types such as COVID-19 infection. Aiforia brings their expertise in AI-supported image analysis to the consortium as a subcontractor. The FUDIS project will establish novel technological platforms, taking point-of-care (POC) tests to a completely new level for more sensitive, faster and accurate results and data management.

Lateral flow assay (LFA) is the most popular platform in point-of-care diagnostics. The platform is easy to use, portable, and low in production costs. However, current LFAs are not highly sensitive, their quantitation capability is limited, and adding several steps to the LFA makes it difficult to perform outside a laboratory by untrained users. FUDIS plans to redefine the LFA platform to be able to tackle more demanding biomarkers.

A number of Lateral Flow tests exist as over-the-counter tests around the world. One drawback is that they are qualitative tests rather than quantitative, which is why implementation of AI can have significant benefits. In addition, in rapid tests for COVID-19 infections, AI can reduce the number of reader-errors or alert the user that the image quality is poor, and advise to retake the image. This increases turn-around time and reduces the burden on the healthcare system.

Aiforia has developed a cloud-based image analysis platform for the analysis of 2D images that utilizes deep learning neural networks, developing quantitative image analysis models for consortium partners including Actim, Helsinki University and VTT as part of the FUDIS project. Aiforia’s AI models have been used in similar proof-of-concept projects funded by Business Finland, such as in the development of Actim’s quantitative LFA-test. The developed concept was an AI-driven point-of-care testing solution to monitor symptoms and disease markers of Inflammatory Bowel Disease (IBD), beneficial to both the IBD patient and the healthcare provider.

“We are very excited to be working with the FUDIS consortium to support diagnostic manufacturers who are developing point-of-care tests. AI will help end-users to interpret test results easier, faster and safer compared to visual reading. Tests can be read anywhere and the result can be moved easily to laboratory or hospital data records” explains Dr. Juuso Juhila, Director of Clinical Products at Aiforia.


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