Intelligent Karyotyping

AI-driven Image Acquisition and Processing for Efficient Karyotyping

Convenient automated workflow for digital karyotyping.
Common chromosome banding techniques are supported.
Different tissue types can be processed.
US Patent for AI based chromosome analysis (U.S. patent no. 10,991,098).
Automated metaphase finding.
High-throughput scanning is configurable for up to 800 slides.
Integrated case and image management.
Extensive reporting functionality.

Time is crucial for karyotyping in routine use. MetaSystems offers a streamlined workflow for digital karyotyping from metaphase finding, chromosome separation to karyogram assignment. Customers using our solutions are able to save processing time and reduce turnaround times.

Our lab had the opportunity to be the first user to test the beta-version of MetaSystems’ new AI-based karyotyping software Ikaros. We experienced a time gain of up to 50% in the karyotype analysis of bone marrow metaphases. This enormous gain in efficiency allows us to keep pace with the ever-increasing workload in times of shortage of personnel resources.

Patented Intelligent Karyotyping Workflow

Software algorithms based on Deep Neural Networks (DNNs), the latest trend in artificial intelligence, facilitate the automated separation and assignment of chromosomes in karyotyping to reduce time-consuming interactive processing steps. For this procedure, MetaSystems was granted a US Patent for AI based chromosome analysis (U.S. patent no. 10,991,098).

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"Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells"

Beate Vajen, Siegfried Hänselmann, Friederike Lutterloh, Simon Käfer, Jennifer Espenkötter, Anna Beening, Jochen Bogin, Brigitte Schlegelberger, Gudrun Göhring

Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. The identification and characterization of chromosomes is a challenging process and needs experienced personal. Artificial intelligence provides novel support tools. However, their safe and reliable application in diagnostics needs to be evaluated. Here, we present a novel laboratory approach to identify chromosomes in cancer cells using a convolutional neural network (CNN). The CNN identified the correct chromosome class for 98.8% of chromosomes, which led to a time saving of 42% for the karyotyping workflow. These results demonstrate that the CNN has potential application value in chromosome classification of hematologic neoplasms. This study contributes to the development of an automatic karyotyping platform.

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Completely Automated Karyogram Proposal with Full User Control

With the Metafer software and suitable imaging hardware, metaphase finding and image acquisition is automated. Subsequently, high-resolution images are directly imported into the Ikaros software for digital karyotyping. The separation of chromosome clusters and karyogram assignment is supported by Deep Neural Networks (DNNs). All automated and manual processing steps are recorded allowing for unlimited undo. Users have access to the original image at any time.

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Easy Upgrading

The new DNN functionality in Ikaros is included in the regular software update path and can easily be implemented in existing Ikaros installations. Interested in an upgrade?

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Non-Human Samples

The Ikaros software also facilitates karyotyping of non-human chromosomes. Ikaros comes with many karyogram forms for different species of animals and plants.

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Are you curious and want to learn more about the possibilities of artificial intelligence in Ikaros? Our team of experts is happy to help!

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