Diagnostic Potential of Imaging Flow Cytometry

Minh Doan, Ivan A. Vorobjev, Paul C. T. Rees, Andrew J. Filby, Olaf Wolkenhauer A, Anne E. Goldfeld, Judy Lieberman, Natasha S. Barteneva, Anne Elizabeth Carpenter, Holger Hennig

2018

Full text Open access: Hybrid Metadata (CSL-JSON)

DOI 10.1016/j.tibtech.2017.12.008

Data confidence 85 high

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.