White blood cells. (Credit: Pixabay/Skeeze)

Researchers have shown that artificial intelligence can detect one of the most common forms of blood cancer — acute myeloid leukemia (AML) — with high reliability. Their approach is based on the analysis of the gene activity of cells found in the blood. Used in practice, this approach could support conventional diagnostics and possibly accelerate the beginning of therapy.

They focused on the “transcriptome,” a kind of fingerprint of gene activity. These data — derived from cells in blood samples and spanning many thousands of genes — were analyzed because they hold important information about the condition of cells.

Data from more than 12,000 blood samples were taken into account: the largest dataset to date for a metastudy on AML. Approximately 4,100 of these blood samples derived from individuals diagnosed with AML, the remaining ones had been taken from individuals with other diseases or from healthy individuals.

The scientists fed their algorithms parts of this data set. The input included information about whether a sample came from an AML patient or not. The algorithms then searched the transcriptome for disease-specific patterns. This was a largely automated process. The researchers say this method could support conventional diagnostics and help save costs.