Roche launches uPath multislide digital pathology software

The software displays multiple types of slides alongside available patient information. (Roche)

Roche has launched its uPath enterprise software for digital pathology, billing it as an improvement in speed and performance compared to its previous version known as Ventana Virtuoso.

According to the big biotech, the workflow software offers automated image analysis and makes it easier to share cases between pathologists and request second opinions. Its development was supported by Leeds Virtual Microscope technology, which Roche acquired from the University of Leeds in the U.K. and the Leeds Teaching Hospitals NHS Trust.

uPath allows pathologists to inspect all slides in a case in a single view, alongside all available patient information. Unlike a typical microscope and glass slides, multiple digital slides appear on a canvas-like display—allowing pathologists to move between hematoxylin and eosin, immunohistochemistry, special stains and others—to help them diagnose a patient faster, Roche said.


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In addition, pathologists can aggregate notes, measurements and slide scoring into sharable images or PDFs, alongside automated and manual slide analyses markups. The virtual microscope system can also scale to display sizes ranging from laptops to high-definition wall screens.

"With this launch, we are able to deliver an improved digital pathology experience consisting of the Ventana DP 200 slide scanner and uPath software, which are the foundation to further enrich our portfolio of automated clinical algorithms for pathologists," Jill German, head of Roche Tissue Diagnostics, said in a statement.

RELATED: Roche, GE ally to develop clinical decision support software

Last year, Roche and GE Healthcare teamed up to develop clinical decision support software, including joint-branded dashboards that collate data from in vitro diagnostics and medical scanners, starting in oncology and critical care.

That software aims to pull in equipment-generated information—including diagnostic images, pathology and genomic information, alongside patient records and research outcomes—for use in machine learning analyses.

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