Better artificial intelligence models to support care decisions

New data analysis technologies emerge all the time, but how to make data available for all researchers?

Finland has exceptionally comprehensive and high-quality health care data resources. The Act on the Secondary Use of Health and Social Data (552/2019) came into effect in 2019 in Finland. Secondary use of data means that customer and register data within social care and healthcare are used for some other than the original purpose. This act on secondary use has also created pressure to amend the Biobank Act from 2013. The significance of data in biomedical research is increasing and the legislation should create the conditions for both research and appropriate data security.

– The biobanks remove all personal identifiers and replace them with pseudonym codes. When samples are handed over for research purposes, the pseudonyms are replaced with another code, specific to that particular research. The code key is stored in the biobank. If you need to access the original sample owing to, for example, by some clinically significant detail, this can only be done with the code key, says Auria Biobank’s Director Lila Kallio.

The act on the secondary use of health and social data concentrated the permit process management to Findata, a new legal authority. Auria’s chief data officer (CDO) and adjunct professor of medical mathematics Arho Virkki points out that material can be used in a number of ways, and that’s why there should be different protection levels based on the purpose of use. Because data management is part of the work of doctors and nurses, Virkki says a balance should be found between material availability and its protection. According to Virkki, amendments are in progress for the act on secondary use of data. If the provisions can be made more flexible and the permit processes faster, there will be many opportunities for artificial intelligence research.

Virkki has been intrigued by AI models for a long time. Recently he has been developing a prediction model for pulmonary embolism. The model is used as a tool for decision-making.

– If there is reason to suspect that a patient in an emergency room has pulmonary embolism, you have to act fast. A machine can quickly go through a set of scanned images and inform the radiologist where they should focus on in any image. After that, the decision is made whether to start diluting or not. If not, another treatment is chosen. You should be able to do all the following in less than 10 minutes: lung imaging, diagnosis and starting the treatment.

The hospital district of Finland Proper uses the CSC´s ePouta cloud service, with a dedicated 10 GiB connection.

In the future, an algorithm may diagnose glaucoma from fundus photos

Glaucoma is a progressive disease of the optic nerve that causes damage to the optic nerve head and nerve fibre layer. The challenging thing about glaucoma is that in its early stages it exhibits no or very few symptoms. Early diagnosis is very important because any damage that has already occurred cannot be reversed. For purposes of glaucoma detection and identification of progressing speed, it would be best if healthcare systems found the high-risk cases as early as possible. Artificial intelligence models are currently being developed for early detection of glaucoma.

Researcher and project manager Ara Taalas specialises in data science, artificial intelligence and machine learning algorithms in medicine. One of his research objectives, in a joint project involving the Institute for Molecular Medicine Finland (FIMM) and Terveystalo health clinic, is to develop effective learning algorithms for glaucoma detection. Previously, Taalas modelled stem cell differentiation processes and worked in drug design.

With artificial intelligence applications, the division of work would probably change dramatically in the optical field and the diagnosis of eye diseases. This would also result in significantly higher numbers of patients being treated. As the age structure of the population is changing, the number of glaucoma patients in Finland will double from the current figures by 2030.

Taalas uses CSC´s computing services. He develops models together with researchers in FIMM’s Machine Learning in Biomedicine team, and the same source code can be used on the computing servers of both CSC and Terveystalo.