Decentralized image acquisition and assessment combined with central case management
Hospitals and private laboratory networks may run numerous laboratories. Many of them have a limited number of Gram slides per day for which an automated scanning and imaging system is not required. On the other hand they have a high interest in standardization to improve testing accuracy.
The Gram Imager* with a manual microscope imaging system is fitting the needs of laboratories with a limited number of Gram slides. The system is based on Metasystems' interactive imaging platform Metafer, which is network enabled, includes a manual microscope with LED light-source and a high-quality CoolCube camera.
Gram Imager stations can easily be integrated into the existing laboratory environment. Hence users can store acquired images in a centralized location enabling access from anywhere in the hospital / laboratory network, speeding up the review process. Moreover pre-defined procedures for reading and assessment can be integrated supporting standardization.
With Neon, MetaSystems offers a solution for case and imaging data organization. Neon collects the data from any imaging job as well as from external sources and presents them in a comprehensive and versatile case management dashboard. Strong data management algorithms, several convenient display modes, and many options for selecting and filtering content make Neon the perfect basis to organize large quantities of information.
With Gram Scanner or Gram Imager, laboratory networks can manage images / cases from all sites equipped with the systems. Create reports, store Gram information with patient files centrally, track workflows and recall all data easily whenever needed, while significantly improving your quality management.
Deep learning techniques can potentially support interpretation of digital Gram images. To match the growing demand for workload reduction and efficiency increase in the review process of Gram stained slides we have integrated deep learning-based object classification using convolutional neural networks (CNN) with our automated scanning platform Metafer to create a unique Gram slide screening environment.
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