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. Detection and accurate characterization of pathological cells is important when making a diagnosis.
Representation of flow cytometric data - as seen by artificial intelligence. Each marker is coded in a different color. This is a hairy cell leukemia that is so rare that only a few large laboratories regularly receive such samples for diagnosis.
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For this reason, Krawitz, together with the bioinformaticians Nanditha Mallesh and Max Zhao, investigated how artificial intelligence can be used to analyze cytometry data. The study has now been published in the journal Patterns.
The team considered more than 30,000 data sets from patients with B-cell lymphoma to train artificial intelligence (AI)."AI takes full advantage of the data and increases the speed and objectivity of diagnoses," says lead author Nanditha Mallesh. The result of the AI evaluations is a suggested diagnosis that still needs to be verified by the physician. In the process, the AI provides indications of conspicuous cells.
Blood samples and cytometer data were obtained from the Munich Leukemia Laboratory (MLL), the Charité - Universitätsmedizin Berlin, the University Hospital Erlangen and the Bonn University Hospital. Specialists from these institutions examined the results of artificial intelligence. "The gold standard is diagnosis by hematologists, which can also take into account results of additional tests," Krawitz says. "The point of using AI is not to replace physicians, but to make the best use of the information contained in the data." The great new feature of the AI now presented lies in the possibility of knowledge transfer: Particularly smaller laboratories that cannot afford their own bioinformatics expertise and may also have too few samples to develop their own AI from scratch can benefit from this. After a short training phase, during which the AI learns the specifics of the new laboratory, it can then draw on knowledge derived from many thousands of data sets.
All raw data and the complete software are open source and therefore freely accessible. In addition, res mechanica GmbH, which was involved in the study, has developed a web service (https://hema.to) that makes artificial intelligence usable even for users without bioinformatics expertise.
The team sees huge potential in this technology. The researchers therefore also want to collaborate with major manufacturers of analytics equipment and software to further advance the use of artificial intelligence.