The artificial intelligence model can identify cancer from digitalised samples

Turku University Hospital and Auria Biobank aim to have all tissue specimens in digital format. The samples would be scanned from glass slides, with diagnostics in pathology performed on computers.Then a pathologist can view the samples on a computer screen and describe and classify them. All this annotation data is relevant to teaching artificial intelligence to automatically detect abnormalities such as cancer cells in the samples. This would considerably speed up the work of pathologists. Auria Biobank has invested in data analytics, development of algorithms, and machine learning models.

Metadata and digitalised sample material are used to develop artificial intelligence applications, for example, which are taught to automatically classify the locations with cancer cells in images. To teach the artificial intelligence system, we require some material classified by pathologists. Auria Biobank’s Director Lila Kallio says that, in addition to genome data being used for research purposes, digital pathology making use of data analytics is a focus area at Auria.

“There is growing interest in how digitalised cancer tissue samples can be used to identify various issues. We are involved in studies where we try to use an algorithm to examine an image of a primary cancer tumour and predict how it will respond to treatment, or whether the primary cancer tumour will metastasise. There are indications that the algorithm may be able to predict something that is not otherwise visible from a histological image.”

According to Lila Kallio, Finland has been a pioneering country in data management and sharing. The Finnish Biobank Act has enabled research and the combination of data from various registers. It is critically important that clinical information can be connected to samples.

According to Lila Kallio, the challenge now and in the future is data storage and management.

“Data is stored inside firewalls in hospital districts. If diagnostic samples will be digitalised on a larger scale within pathology, the storage capacity problem must be solved as well. In addition, the image sizes are so huge that they cannot be transferred on ordinary data networks.

The Finnish ELIXIR Center CSC plays an important role in terms of computing power, and safe storage and usage environments.

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The artificial intelligence model can identify cancer from digitalised samples

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