In clinical cytogenetics, laboratory professionals analyze numerical and structural aberrations in chromosomes to diagnose genetic diseases or cancer. Deep Neural Networks (DNNs), a recent innovation in artificial intelligence, separate and classify banded chromosomes to generate karyogram proposals in Ikaros.

Optimizing the counting of FISH spots can be a challenging task for laboratories in cytogenetics and hematology. By customizing the functionalities of the slide scanning software Metafer, it is possible to replace manual FISH spot counting with a swift workflow involving automated microscopy and interpretation by the expert.

MetaSystems has adapted an AI-supported workflow that automates the acquisition of slides and pre-catagorizes detected blood cells. The solution includes a real-time image gallery and a dedicated RapidScore keyboard for quick validation and re-classification. 

Our proposed Metafer-based workflow for forensic sperm detection, implemented and validated by users worldwide, leverages artificial intelligence with Deep Neural Networks (DNN) to detect sperm cells in standard forensic samples, even in challenging conditions such as degradation, concealment, or absence of tails.

Sperm cells are a crucial piece of evidence for solving sexual crimes and are usually detected by microscopy. Using deep learning algorithms implemented in the Metafer slide scanning software, MetaSystems has put together a workflow that brings the benefits of artificial intelligence to the field of forensic microscopy.