Abstract
- The lack of standardization in panel design and the complexity of manual analysis for flow cytometry (FC) data have hindered the speed and the spread of both implementation and the service capacity of flow cytometry being in the emerging countries.
- This study employs multiple machine learning (ML) approaches to develop an automated panel-agnostic AML classification at both sample and cell levels from several distinct flow cytometry laboratories.