Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.
Journal
Methods in molecular biology (Clifton, N.J.)
Journal Volume
2779
Pages
353 - 367
ISSN
1940-6029
Date Issued
2024
Author(s)
Abstract
Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.
Subjects
Acute myeloid leukemia
Artificial intelligence
Automated classification
Flow cytometry
Machine learning
SDGs
Type
journal article