The presence of cancer of the lymphatic system is often determined by analyzing samples from the blood or bone marrow. A team led by Prof. Dr. Peter Krawitz from the University of Bonn had already shown in 2020 that artificial intelligence can help with the diagnosis of such lymphomas and leukemias. The technology fully utilizes the potential of all measurement values and increases the speed as well as the objectivity of the analyses compared to established processes. The method has now been further developed so that even smaller laboratories can benefit from this freely accessible machine learning method - an important step towards clinical practice. The study has now been published in the journal Patterns.

Lymph nodes become swollen, there is weight loss and fatigue, as well as fevers and infections - these are typical symptoms of malignant B-cell lymphomas and related leukemias. If such a cancer of the lymphatic system is suspected, the physician takes a blood or bone marrow sample and sends it to specialized laboratories. This is where flow cytometry comes in. Flow cytometry is a method in which the blood cells flow past measurement sensors at high speed. The properties of the cells can be detected depending on their shape, structure or coloring.

The presence of cancer of the lymphatic system is often determined by analyzing samples from the blood or bone marrow. A team led by Prof. Dr. Peter Krawitz from the University of Bonn had already shown in 2020 that artificial intelligence can help with the diagnosis of such lymphomas and leukemias. The technology fully utilizes the potential of all measurement values and increases the speed as well as the objectivity of the analyses compared to established processes. The method has now been further developed so that even smaller laboratories can benefit from this freely accessible machine learning method – an important step towards clinical practice. The study has now been published in the journal "Patterns".