Artificial intelligence detects cancer and viral infections with nanometric precision

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 University of the Basque Country (UPV/EHU), the DIPC (Donostia International Physics Center), the Fundación Biofísica Bizkaia (FBB) and the Centre for Genomic Regulation (CRG) in Barcelona, ​​has developed an artificial intelligence (AI) that can differentiate cancer cells from normal cells, as well as detect the earliest stages of viral infection inside cells. These findings, presented publicly through the academic journal Nature Machine Intelligence, pave the way for developing new diagnostic techniques and disease monitoring strategies.

The tool, AINU (AI of the NUcleus), scans high-resolution images of cells. The images are obtained using a special microscopy technique called STORM, which creates an image that captures far more detail than can be seen with normal microscopes. The high-resolution snapshots reveal structures at nanoscale resolution.

A nanometer (nm) is one billionth of a meter, and an individual human hair is about 100,000 nm thick. Artificial intelligence can detect rearrangements within cells as small as 20 nm, or 5,000 times smaller than the thickness of a human hair. These alterations are too small and subtle for human observers to detect using 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 inside cells, helping to detect alterations very soon after they occur. We believe that, one day, this type of information may allow doctors to gain time to monitor the disease, personalise treatments and improve patient outcomes,” says ICREA Research Professor Pia Cosma, co-author of the study and researcher at the Centre for Genomic Regulation.

“Facial recognition” at the molecular level

AINU is a convolutional neural network, a type of artificial intelligence specifically designed to analyze visual data such as images. Examples include AI tools that allow users to unlock smartphones with their face or tools that self-driving cars use to understand and navigate environments by recognizing objects on the road.

In medicine, convolutional neural networks are used to analyze medical images such as mammograms or CT scans and identify signs of cancer that the human eye might miss. They can also help doctors detect abnormalities in MRI or X-ray images, leading to faster and more accurate diagnoses.

AINU detects and analyses tiny structures inside cells at the molecular level. The scientific team trained the model by feeding it nanometer-resolution images of the nucleus of many different types of cells in different states. The model learned to recognise specific patterns in cells by analysing how nuclear components are distributed and organised in three-dimensional space.

For example, cancer cells have distinctive changes in their nuclear structure compared to normal cells, such as alterations in the way their DNA is organized or the distribution of enzymes within the nucleus. After training, AINU was able to analyze new images of cell nuclei and classify them as cancerous or normal based on these features alone.

The nanoscale resolution of the images allowed the AI ​​to detect changes in the nucleus of a cell just one hour after it was infected by the herpes simplex virus type 1. The model can detect the presence of the virus by finding tiny differences in DNA density, which occurs when a virus begins to alter the structure of the cell’s nucleus.

“Our method can detect cells that have been infected by a virus very soon after the infection begins. Normally, doctors take a while to detect an infection because they rely on visible symptoms or larger changes in the body. But with AINU, we can see small changes in the cell nucleus right away,” says Ignacio Arganda-Carreras, co-author of the study and Ikerbasque associate researcher at the UPV/EHU and affiliated with the FBB-Instituto Biofísica and the DIPC in San Sebastián/Donostia.

“This technology can be used to see how viruses affect cells almost immediately after entering the body, which could help develop better treatments and vaccines. In hospitals and in the clinic, AINU could be used to diagnose infections from a simple blood or tissue sample, making the process faster and more accurate,” adds Limei Zhong, co-author of the study and researcher at the Guangzhou Provincial People’s Hospital (GDPH) in Guangzhou, China.

Ultra-high-resolution image of a HeLa cancer cell. The image uses two colors to show nuclear components that allow detailed structures within the cell nucleus to be seen at nanometer resolution. (Photo: Limei Zhong)

Laying the foundation for clinical preparation

The study’s authors caution that there are still important limitations to overcome before the technology is ready to be tested or implemented in a clinical setting. For example, STORM images can only be taken with specialized equipment typically found only in biomedical research laboratories. Setting up and maintaining the imaging systems required by artificial intelligence is a significant investment in both equipment and technical skills.

Another limitation is that STORM imaging analyzes only a few cells at a time. For diagnostic purposes, especially in clinical settings where speed and efficiency are crucial, doctors would need to capture many more cells in a single image to be able to detect or monitor a disease.

“There are many rapid advances in the field of STORM imaging, which means that microscopes may soon be available in smaller or less specialized laboratories and eventually even in the clinic. Accessibility and performance limitations are more manageable problems than we thought and we hope to perform preclinical experiments soon,” explains Dr. Cosma.

Although clinical benefits may take years to arrive, AINU is expected to accelerate scientific research in the near term. The study’s authors found that the technology can also identify stem cells with very high accuracy. These cells can develop into any type of cell in the body and are studied for their potential to help repair or replace damaged tissue.

AINU can make the stem cell detection process faster and more accurate, and would help make the resulting therapies safer and more effective. “Current methods for detecting 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. As well as being easier and faster, it can speed up stem cell research while also contributing to the shift towards reducing the use of animals in science,” concludes Davide Carnevali, co-author of the study and researcher at the CRG. (Source: UPV/EHU)