Patent Document ID: 9934364
Application ID: 15445913
Patent Flag: 1

Claim One:
1. A computer-implemented method of applying artificial neural networks to a plurality of events of interest in a biological sample from a subject to generate a medical diagnosis and a treatment recommendation of a cancer in the subject, the artificial neural network capable of continually training, comprising: (a) performing, by a computer, analysis of the biological sample from the subject, the analysis comprising: 1) obtaining measurements of a plurality of event features for each of the plurality of events of interest from a flow cytometer instrument, 2) using four or more flow cytometer measurement channels to define a feature coordinate space, the feature coordinate space comprising four or more axes, each axis corresponding to a different channel of the four or more flow cytometer measurement channels, wherein each of the four or more flow cytometer measurement channels produces measurements of the plurality of event features, and 3) using the measurements of the plurality of event features for the plurality of events of interest to define locations for the plurality of events of interest in the feature coordinate space to form a distribution in the feature coordinate space indicative of an event population of interest, wherein the distribution in the feature coordinate space indicative of the event population of interest is formed by: (i) dividing each axis of the feature coordinate space into a plurality of segments, thereby dividing the coordinate space into a plurality of hypervoxels, and (ii) for each hypervoxel of the plurality of hypervoxels, determining a count of a number of events of interest comprising an event feature value that locates the event of interest in the hypervoxel; (b) applying, by the computer, an artificial neural network detection structure to the distribution in the feature coordinate space indicative of the event population of interest, the detection structure employing an artificial neural network to correlate the distribution in the feature coordinate space indicative of the event population of interest with a distribution in a reference feature coordinate space indicative of a reference event population; (c) determining, by the computer, whether the biological sample contains cells indicative of the cancer in the subject, thereby diagnosing the cancer in the subject; (d) identifying, by the computer, characteristic cell features of cells indicative of the cancer; (e) automatically generating, by the computer, a report comprising the medical diagnosis and the treatment recommendation for the cancer in the subject, wherein the generating is based on the cell features indicative of the cancer; and (f) using a subject status and cell feature data to further train the artificial neural network.