Since 1986 MetaSystems has been designing and manufacturing systems for computerized automated microscopic imaging. From the very beginning, the close relationship to the end user has been an essential part of the MetaSystems philosophy. The following three decades have revealed that this was a successful strategy; from humble beginnings, MetaSystems has grown into a leading solution provider which still remains connected to its strong roots.
MetaSystems’ latest innovation, Neon, is a dynamic, modern imaging platform which reliably manages cases, images, and results from single workstation environments to large multi-user, multi-site installations. The Neon imaging platform ensures that all relevant information is accessible whenever needed.
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Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum smears remains a crucial first-line detection method. However, it puts considerable workload on laboratory staff and suffers from a limited sensitivity. Here we validate a scanning and analysis system that combines fully-automated microscopy with deep-learning based image analysis. After automated scanning, the system summarizes diagnosis-relevant image information and presents it to the microbiologist in order to assist diagnosis. We tested the benefit of the automated scanning and analysis system using 531 slides from routine workflow, of which 56 were from culture positive specimen. Assistance by the scanning and analysis system allowed for a higher sensitivity (40/56 positive slides detected) than manual microscopy (34/56 positive slides detected), while greatly reducing manual slide-analysis time from a recommended 5-15 min to around 10 s per slide on average.
Cited From: Tuberculosis (Edinburgh, Scotland) (2020)
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Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. [...] Here we validate a scanning and analysis system that combines fully-automated microscopy with deep-learning based image analysis.
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