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NEW YORK ─ Researchers at the National Institutes of Health's National Cancer Institute are leading the development of a machine-learning-based dual-staining technology with a goal of enabling more accurate cervical cancer screening.

Their method ─ which uses an algorithm to identify and analyze protein biomarkers associated with abnormal cells on slide images ─ takes aim at helping clinicians reduce unnecessary follow-up testing and invasive procedures, according to Nicolas Wentzensen, a researcher at NCI who is leading the development of the technology with colleagues.

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Aug
19
Sponsored by
UgenTec

This webinar will present a case study from in vitro diagnostics developer SpeeDx on its experience building a complete sample-to-result workflow — encompassing instrumentation and data analysis software — for its qPCR-based ResistancePlus MG Mycoplasma genitalium assay.