Systems and methods for detecting particles of interest using multi-model spectral analysis

A method comprising:

• receiving data, the data including a set of spectral metrics, the data from an apparatus that obtains the set of spectral metrics based on interactions of electromagnetic radiation with a sample;
• applying a first trained model to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a first result, the first trained model trained on a first set of training samples for first particles of interest and a second set of training samples for second particles of interest, the first particles of interest including at least a first type of the first particles of interest and a second type of the first particles of interest;
• applying a second trained model to at least one of the set of spectral metrics and the set of values to obtain a second result, the second trained model being different than the first trained model and trained on a third set of training samples for the first type of the first particles of interest and a fourth set of training samples for the second type of the first particles of interest;
• based on at least one of the first result and the second result, determining either a positive particle of interest detection or a negative particle of interest detection for at least one of the first particles of interest, the first type of the first particles of interest, and the second type of the first particles of interest for the sample;
• generating a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection;
• and
• providing the particle of interest detection notification.

A system comprising at least one processor and memory containing executable instructions, the executable instructions being executable by the at least one processor to:

• receive data, the data including a set of spectral metrics, the data from an apparatus that obtains the set of spectral metrics based on interactions of electromagnetic radiation with a sample;
• apply a first trained model to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a first result, the first trained model trained on a first set of training samples for first particles of interest and a second set of training samples for second particles of interest, the first particles of interest including at least a first type of the first particles of interest and a second type of the first particles of interest;
• apply a second trained model to at least one of the set of spectral metrics and the set of values to obtain a second result, the second trained model being different than the first trained model and trained on a third set of training samples for the first type of the first particles of interest and a fourth set of training samples for the second type of the first particles of interest;
• based on at least one of the first result and the second result, determine either a positive particle of interest detection or a negative particle of interest detection for at least one of the first particles of interest, the first type of the first particles of interest, and the second type of the first particles of interest for the sample;
• generate a particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection;
• and
• provide the particle of interest detection notification.