Swansea Uni researchers develop way to identify different cell types using AI
Researchers at Swansea University have developed a new way to identify different cell types, including cancer cells, using artificial intelligence.
The method, which has been discovered at the university’s college of engineering, involves training computers to detect them using AI algorithms. It uses a similar approach to face and fingerprint recognition software.
This research project is a collaboration between Swansea University, the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, USA; Helmholtz Zentrum Munchen in Munich, Germany; the Francis Crick Institute in London; and Newcastle Upon Tyne University.
Their findings have been published in a research paper entitled ‘Label-free cell cycle analysis for high-throughput imaging flow cytometry’, which has published in life sciences journal Nature Communications.
Professor Paul Rees, who’s from Swansea University’s College of Engineering and an author of the paper, said: “To identify different types of cells, e.g. cancer cells, within a health cell population, scientists usually have to use special fluorescent stains that bind to components of the cell to allow detection using microscopy.
“Unfortunately these stains alter the cell’s behaviour and modify the system being investigated.
“The new method we have developed avoids the use of these stains using AI machine learning algorithms. The researchers train the algorithm to recognise the specific cell of interest by giving examples of the cell to be identified.
“After learning what the cells look like, the computer algorithms can then identify the target cells in a population of previously unseen cells.”
According to the researchers, the method is accurate enough to determine the exact position of the cell during its life cycle.
“Most anti-cancer treatments act specifically on cells at a certain point within their life cycle and therefore it is highly desirable to determine the age of cells in a population, without perturbing them with stains,” continued Professor Rees.
Dr Fabian Theis of the Helmholtz Zentrum Munchen added: “Computer-based classification of cells with algorithms opens up a whole new perspective that could also be used for entirely different research questions, not only for cell cycle analysis.”
Image credit: Yale Rosen