A new artificial intelligence (AI) tool capable of differentiating between cancer cells and non-cancerous cells, and detecting early-stage viral infections, was unveiled by a group of researchers from the Center for Genomic Regulation (CRG), the University of the Basque Country (UPV/EHU), the Donostia International Physics Center (DIPC), and the Bizkaia Foundation for Physics (FBB), according to a recent article in the scientific magazine “Nature.”
The AI system, known as AINU (AI of the NUcleus), employs high-resolution images of cells captured through a specialized microscopy technique called STORM (Stochastic Optical Reconstruction Microscopy).
The method offers images captured at a nanoscale resolution, enabling visualization of cellular structures that cannot be seen by conventional microscopes.
The AI software was able to detect changes in the infected cell’s nucleus within just one hour of exposure to the herpes simplex virus type-1, thanks to the high-resolution images. The model successfully identified the virus by detecting slight alterations in the DNA structure, which occur when a virus begins to modify the cell nucleus.
“The researchers trained the model by feeding it with nanoscale-resolution images of the nucleus of many different types of cells in different states. The model learned to recognize specific patterns in cells by analyzing how nuclear components are distributed and arranged in three-dimensional space,” the specialized outlet “News Medical” reported.
“Our method can detect cells that have been infected by a virus very soon after the infection starts. Normally, it takes time for doctors to spot an infection because they rely on visible symptoms or larger changes in the body. But with AINU, we can see tiny changes in the cell’s nucleus right away,” Ignacio Arganda-Carreras, Research Associate at UPV/EHU and co-corresponding author of the study stated.
“We think that one day, this type of information can buy doctors valuable time to monitor disease, personalize treatments, and improve patient outcomes,” Pia Cosma, ICREA Research Professor, co-corresponding author of the study, and researcher at the Centre for Genomic Regulation, Barcelona said.
The field of STORM imaging, according to Cosma, is experiencing rapid advances, which may soon make microscopes available in smaller or less specialized labs, and eventually, even in the clinic.
She added that limitations of accessibility are more tractable problems than we previously thought and that they aim to carry out preclinical experiments soon.
“Current methods to detect high-quality stem cells rely 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,” Davide Carnevali, first author of the research and researcher at the CRG commented.
AINU has yet to overcome several hurdles before it can be deployed in a clinical setting. The high cost and limited availability of STORM equipment, and its capacity to analyze only a few cells at a time, are among the challenges that must be addressed.
However, the technology has the potential to significantly enhance scientific research, particularly in the field of stem cell studies.