Automated classification of biological subpopulations using impedance parameters

A method of automated classification of a biological specimen, the method comprising:

• receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters;
• within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies;
• extracting at least two electrical impedance parameters from the measured electrical impedance;
• labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model;
• and
• using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function.

A method for inline classification of biological structures using a machine learning technique informed by a biological specimen, the method comprising:

• receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters;
• within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies;
• extracting at least two electrical impedance parameters from the measured electrical impedance;
• using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function;
• and
• recycling at least a portion of the analyte biological specimen back through the test cell.