Abstract: Methods, systems, and apparatus for medical diagnosis using cytometry data. One method includes receiving training flow cytometry data and obtaining a superclass-subclass relationship between a plurality of diagnosis labels in a diagnosis label space, wherein a diagnosis label represents a medical condition based on the training flow cytometry data. The method also includes, via the electronic processor, encoding cytometry domain knowledge into a plurality of soft constraints including a relationship constraint representing a correlation between a pair of diagnosis labels of the plurality of diagnosis labels and a hierarchical constraint representing the superclass-subclass relationship and training a machine learning model using a loss function including the plurality of soft constraints.

Abstract: Systems, methods, and apparatuses for performing real-time cytometry data analysis. One apparatus includes at least one electronic processor and at least one memory storing instructions executable by the at least one electronic processor. The at least one electronic processor is configured, through execution of the instructions, to obtain flow cytometry data generated by a cytometry instrument representing cells of multiple categories, generate a feature vector representation based on the flow cytometry data using a plurality of self-organizing maps (SOMs), wherein each SOM corresponds to a different category of multiple categories, and predict each of one or more target labels of the cells by applying each of one or more regression models to the feature vector representation.

Abstract: Systems, methods, and apparatuses for performing real-time data analytics. One apparatus includes at least one electronic processor and at least one memory storing instructions executable by the at least one electronic processor. The at least one electronic processor is configured to obtain a sequence of cytometry data output by a cytometry instrument, normalize a first data point in the sequence of cytometry data using a set of normalization parameters determined based on a queue of cases belonging to a predetermined class, update the queue of cases belonging to the predetermined class with the normalized first data point in response to determining, using a machine learning model, the normalized first data point belongs to the predetermined class, and normalize a second data point in the sequence of cytometry data using an updated set of normalization parameters determined based on the updated queue of cases belonging to the predetermined class.

Abstract: Disclosed herein is a detection system configured to assess the density of acid-fast bacteria (AFB) in a sputum smear that has been stained with a non-fluorescent staining technique. The computer system assesses the density of AFB in a sputum smear using one or more object detection models, wherein the object detection models are configured to identify AFB stained using a non-fluorescent staining process. The disclosed system can increase throughput and accuracy of AFB density assessment in a diagnostic laboratory setting and can improve throughput and accuracy of more cost-effective methods of assessing AFB density such as a Kinyoun staining process.