Study finds AI system that analyse cells for faster diagnosis of blood diseases

WASHINGTON DC: Researchers have created an artificial intelligence (AI) system that can identify and classify white and red blood cells in microscopic images of blood samples.

The algorithm is a free tool for research that can assist doctors in the diagnosis of blood disorders.

Red and white blood cells' abnormal shapes and altered numbers are frequent indicators of blood disorders.

Additionally, doctors use a microscope to examine blood smears on a slide to diagnose diseases. This type of diagnosis is simple, but evaluation by skilled professionals is challenging because the changes can occasionally be very subtle and affect only a small number of the hundreds of thousands of visible cells.

These challenges make it challenging to distinguish between diseases. For instance, in myelodysplastic syndrome (MDS), an early form of leukaemia, patients' visible blood changes frequently resemble those of much more benign forms of anaemia.

Therefore, additional, more invasive procedures are needed to make a conclusive diagnosis of MDS, such as molecular genetic testing and analysis of bone marrow biopsies.

"To support specialists in these difficult diagnoses, we have developed a computer-based system that automatically recognizes and characterizes white and red blood cells from peripheral blood," explained Moritz Gerstung of DKFZ.

Gerstung and colleagues first trained the algorithm, called Haemorasis, to recognize the cell morphology of more than half a million white blood cells as well as many millions of red blood cells from more than 300 individuals with different blood disorders (various anaemias and forms of MDS).

"The algorithm is able to detect the shape and number of tens of thousands of blood cells in a microscopic image of the blood. This complements human capabilities, which are typically more focused on detail," Gerstung said.

Using the trained knowledge, Haemorasis can now suggest diagnoses of blood disorders and even distinguish genetic subtypes of the diseases. In addition, the algorithm also reveals concrete correlations between certain cell morphologies and diseases, which are often difficult to find because of the large number of cells involved.

Haemorasis has already been tested on three independent groups of patients to demonstrate that the system also works in other test centres and blood count scanner systems "We have now demonstrated for the first time that computer-assisted analysis of blood images is possible and can contribute to initial diagnosis," explained Gerstung.

Haemorasis is designed to facilitate diagnostics in haematology and can help make a more accurate initial diagnosis of blood disorders. This is important for identifying those patients who require more invasive testing, such as bone marrow punctures or genetic analysis.

"Automated cell analysis with Haemorasis could complement routine diagnosis of blood disorders in the future. So far, the algorithm has only been trained on specific diseases - but we still see great potential in this approach," Gerstung said.