AI and Large Bone Marrow Cell Data Set Help to Identify Blood Diseases

A data set of more than 170,000 microscopic images allows for the training of neural networks for the identification of bone marrow cells with high accuracy.

 

Diagnosing blood disorders relies on a century-old method of using optical microscopes to analyze and classify samples of bone marrow cells. The method used to look for rare, but diagnostically important, cells is well-established, albeit laborious and time-consuming. Artificial intelligence (AI) has the potential to improve this method. However, training an AI algorithm requires a large amount of high-quality data. Now a team has used a data set of more than 170,000 microscopic images to train neural networks to identify bone marrow cells with high accuracy.

 

The Helmholtz Munich researchers developed the largest open-access database on microscopic images of bone marrow cells to date. The database consists of more than 170,000 single-cell images from over 900 patients with various blood diseases. It is the result of a collaboration from Helmholtz Munich with the LMU University Hospital Munich, the MLL Munich Leukemia Lab (one of the largest diagnostic providers in this field worldwide), and Fraunhofer Institute for Integrated Circuits.

 

This work was published in Blood, in the paper, “Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.

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