| CPC G16B 40/10 (2019.02) [G16H 10/40 (2018.01); G16H 15/00 (2018.01); G16H 50/70 (2018.01)] | 15 Claims |
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1. An artificial intelligence (AI)-based system for automatically identifying biological molecules in a fluid sample, comprising:
one or more spectrometers or chemical analysis devices;
at least one reactor;
one or more servers configured to receive multi-dimensional training data from the one or more spectrometers or chemical analysis devices; and
an AI module on the one or more servers configured to automatically develop characteristic profiles for a plurality of molecules or sets of molecules based on the multi-dimensional training data;
wherein the multi-dimensional training data corresponds with samples of known molecules and/or compositions;
wherein the multi-dimensional training data includes data for at least one baseline of at least one healthy sample;
wherein the multi-dimensional training data further includes synthetic data;
wherein the at least one reactor is operable to ionize a medical fluid sample via a non-thermal plasma source;
wherein the one or more servers receives experimental data for the ionized medical fluid sample from a testing spectrometer;
wherein the AI module is operable to compare the experimental data with the data for the at least one baseline of the at least one healthy sample;
wherein the AI module is operable to analyze the experimental data, wherein the AI module automatically correlates spectrographic data from the experimental data with the characteristic profiles to identify biological molecules in the medical fluid sample;
wherein the AI module automatically generates a report based on comparison of the experimental data to the characteristic profiles for the plurality of molecules or sets of molecules; and
wherein the report includes indications of present identified molecules, indications of concentrations of the present identified molecules, and certainty values associated with each of the present identified molecules.
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