Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

ABSTRACT: Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable automated counter has yet to be developed, largely due to the inherent complexity of bone marrow specimens. Digital pathology imaging coupled with machine learning algorithms represents a highly promising emerging technology for this purpose. Yet, training datasets for BMA cellular constituents, critical for building and validating machine learning algorithms, are lacking. Herein, we report our experience creating and employing such datasets to develop a machine learning algorithm to detect and classify BMA cells. Utilizing a web-based system th

SUBMITTER: Chandradevan R 

PROVIDER: S-EPMC6920560 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature