The combination of artificial intelligence with the development of new microscopic technologies to obtain high-resolution images of cells opens the way to new strategies for diagnosing and monitoring diseases.
A scientific team from the Centre for Genomic Regulation (CRG), the University of the Basque Country (UPV/EHU), the Donostia International Physics Center (DIPC) and the Fundación Biofísica Bizkaia (FBB, located at the Biophysics Institute) has developed an artificial intelligence that can differentiate cancer cells from normal cells, as well as detect the earliest stages of viral infection inside cells. The findings, published in the journal Nature Machine Intelligence, pave the way for the development of new diagnostic techniques and disease monitoring strategies.
The tool, AINU (AI of the NUcleus), scans high-resolution images of cells. The images are obtained with a special microscopy technique called STORM, which creates an image that captures far more detail than normal microscopes can see. The high-definition snapshots reveal structures with nanometre-scale resolution.
A nanometre (nm) is one billionth of a metre, and an individual strand of human hair is about 100,000 nm wide. AI can detect rearrangements within cells as small as 20 nm, or 5,000 times smaller than the width of a human hair. These alterations are too small and subtle for human observers to detect with traditional methods.
‘The resolution of these images is powerful enough for our AI to recognise specific patterns and differences with remarkable accuracy, including changes in the way DNA is organised within cells, helping to detect alterations very soon after they occur. We believe that, one day, this kind of information may allow doctors to gain time to monitor disease, personalise treatments and improve patient outcomes,’ says ICREA research professor Pia Cosma, co-lead author of the study and a researcher at the Centre for Genomic Regulation (CRG) in Barcelona.
Laying the groundwork for clinical preparedness
The study authors caution that important limitations still need to be overcome before the technology is ready to be tested or implemented in a clinical setting. For example, STORM images can only be taken with specialised equipment normally found only in biomedical research laboratories. The installation and maintenance of the imaging systems required for AI is a major investment in both equipment and technical skills.
Another limitation is that STORM imaging analyses only a few cells at a time. For diagnostic purposes, especially in clinical settings where speed and efficiency are crucial, clinicians would need to capture many more cells in a single image in order to detect or monitor a disease.
Although clinical benefits may take years to arrive, in the short term, it is expected that AINU will accelerate scientific research. AINU could make the process of detecting stem cells faster and more accurate, and would help make the resulting therapies safer and more effective.
‘Current methods for detecting high-quality stem cells are based on animal testing. However, all our AI model needs to work is a sample that is stained with specific markers that highlight key nuclear features. As well as being easier and faster, it can speed up stem cell research and, at the same time, contribute to the shift away from the use of animals in science,’ concludes Davide Carnevali, first author of the study and CRG researcher.
