q&more
My watch list
my.chemie.de  
Login  

News

AI-driven single blood cell classification

New method to support physicians in leukemia diagnostics

© Helmholtz Zentrum München / Carsten Marr

Deep learning algorithms of AI analyses samples in an automated and standardized way. Left: What human experts classify. Right: Pixels important for AI analysis.

14-Nov-2019: For the first time, researchers from Helmholtz Zentrum München and the University Hospital of LMU Munich show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia (AML). Their proof of concept study paves the way for an automated, standardized and on-hand sample analysis in the near future.

Every day, millions of single blood cells are evaluated for disease diagnostics in medical laboratories and clinics. Most of this repetitive task is still done manually by trained cytologists who inspect cells in stained blood smears and classify them into roughly 15 different categories. This process suffers from classification variability and requires the presence and expertise of a trained cytologist.

To improve evaluation efficiency, a team of researchers at Helmholtz Zentrum München and the University Hospital, LMU Munich, trained a deep neuronal network with almost 20.000 single cell images to classify them. The team lead Dr. Carsten Marr and medical doctoral student Dr. Christian Matek from the Institute of Computational Biology at Helmholtz Zentrum München as well as Prof. Dr. med Karsten Spiekermann and Simone Schwarz from the Department of Medicine III, University Hospital, LMU Munich, used images which were extracted from blood smears of 100 patients suffering from the aggressive blood disease AML and 100 controls. The new AI-driven approach was then evaluated by comparing its performance with the accuracy of human experts. The result showed that the AI-driven solution is able to identify diagnostic blast cells at least as good as a trained cytologist expert.

Applied research through AI and Big Data

Deep learning algorithms for image processing require two things: first, an appropriate convolutional neural network architecture with hundreds of thousands of parameters; second, a sufficiently large amount of training data. So far, no large digitized dataset of blood smears has been available, although these samples are used pervasively in clinics. The research group at Helmholtz Zentrum München now provided the first large data set of that type. Currently, Marr and his team are collaborating closely with the Department of Medicine III at the University Hospital of LMU Munich and one of the largest European Leukemia laboratories, the Munich Leukemia Laboratory (MLL), to digitalize hundreds of patient blood smears more.

“To bring our approach to clinics, digitization of patients’ blood samples has to become routine. Algorithms have to be trained with samples from different sources to cope with the inherent heterogeneity in sample preparation and staining,” says Marr. “Together with our partners we could prove that deep learning algorithms show a similar performance as human cytologists. In a next step, we will evaluate how well other disease characteristics, such as genetic mutations or translocations, can be predicted with this new AI-driven method.”

This method showcases the applied power of AI for translational research. It is an extension of the pioneering work of Helmholtz Zentrum München on single cell classification in blood stem cells (Buggenthin et al., Nature Methods, 2017) which has been awarded with the Erwin Schroedinger Prize of the Helmholtz Association in 2018. The study was supported by the SFB 1243 of the German Research Foundation (DFG) and by a PhD scholarship of the German José Carreras Leukaemia Foundation to Dr. Christian Matek.

Original publication:
Matek, C. et al.; "Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks"; Nature Machine Intelligence; 2019

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt GmbH

Request information now

Recommend news PDF version / Print Add news to watchlist

Share on

Facts, background information, dossiers

  • deep learning
  • acute myeloid leukemia
  • blood analysis
  • artificial intelligence

More about Helmholtz Zentrum München

More about LMU

  • News

    An ultrafast glimpse of the photochemistry of the atmosphere

    Researchers at Ludwig-Maximilians-Universitaet (LMU) in Munich have explored the initial consequences of the interaction of light with molecules on the surface of nanoscopic aerosols. The nanocosmos is constantly in motion. All natural processes are ultimately determined by the interplay be ... more

    DNA repair: Opening the hatch to heal the break

    LMU researchers have determined the structure of a key enzyme complex that is involved in DNA repair, and traced the cycle of conformational changes that it undergoes while performing its biochemical function. Various types of DNA damage can have serious repercussions, both for the individu ... more

    Alzheimer’s disease: Protective immune response in the brain?

    LMU researchers Christian Haass and Michael Ewers have identified a factor that might possibly delay the emergence and slow the progression of Alzheimer’s disease. Researchers at the German Center for Neurodegenerative Diseases (DZNE) and the Institute for Stroke and Dementia Research (ISD) ... more

  • Authors

    Prof. Dr. Thomas Carell

    Thomas Carell graduated in chemistry, completing his doctorate at the Max Planck Institute for Medical Research under the tutelage of Prof. Dr Dr H. A. Staab. Following a research position in the USA, he accepted a position at ETH Zurich, setting up his own research group in the Laboratory ... more

q&more – the networking platform for quality excellence in lab and process

The q&more concept is to increase the visibility of recent research and innovative solutions, and support the exchange of knowledge. In the broad spectrum of subjects covered, the focus is on achieving maximum quality in highly innovative sectors. As a modern knowledge platform, q&more offers market participants one-of-a-kind networking opportunities. Cutting-edge research is presented by authors of international repute. Attractively presented in a high-quality context, and published in German and English, the original articles introduce new concepts and highlight unconventional solution strategies.

> more about q&more

q&more is supported by:

 

Your browser is not current. Microsoft Internet Explorer 6.0 does not support some functions on Chemie.DE