Rare event detection based on artificial intelligence utilizes autonomous confocal microscopy technology
On June 6, 2023, Leica Microsystems, a leading company in the field of microscopes and scientific instruments in Wetzlar, Germany, launched its Aivia driven autonomous microscope. This new artificial intelligence detection workflow is used for confocal microscopy imaging and can automatically detect rare events. It triggers rare event scanning based on user-defined objects of interest. Up to 90% of rare events can be automatically detected in experiments, enabling users to make more discoveries. This method focuses on important data during the image acquisition process, so the time required to obtain experimental results can be reduced by up to 70%. The workflow driven by Aivia can reduce microscope usage time by up to 75% and improve user efficiency.
The autonomous microscope driven by Aivia uses a simple and easy-to-use approach to bring the powerful power of artificial intelligence into daily experimental environments James O'Brien, Vice President of Life Sciences and Applied Microscopy Imaging at Leica Microsystems, said so. Now, researchers can create confocal microscopy imaging workflows to complete advanced experiments that cannot be done without automated programs or are very tedious, as well as to study such biological problems. This solution provides them with excellent new options, allowing them to obtain useful experimental results for their research problems
The workflow for rare event detection is based on the interaction between two available components on the STELLARIS confocal system. Usually, an overview scan of the biological sample is analyzed first. If Aivia's artificial intelligence image analysis software detects indicators of rare events, the locations of these events will be sent back to STELLARIS' control software Navigator Expert. Then, based on user-defined settings, automatically scan the location of identified events in high resolution and 3D.
Using Aivia driven autonomous microscopy imaging, the operator's human-machine interaction is limited to initial settings. Can detect objects more quickly and accurately. The same settings can be used for other experiments to ensure consistency. Because it only identifies and captures objects of interest, it can significantly reduce data collection and final analysis time. This specificity also means that storage space can be greatly saved.
