Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies.
Journal:
American journal of clinical pathology
PMID:
33210119
Abstract
OBJECTIVES: Clinical flow cytometry is laborious, time-consuming, and expensive given the need for data review by highly trained personnel such as technologists and pathologists as well as the significant number of normal cases. Given these issues, automation in analysis and diagnosis holds the key to major efficiency gains. The objective was to design an automated pipeline for the diagnosis of B-cell malignancies in flow cytometry and evaluate its performance against our standard clinical diagnostic flow cytometry process.