System and method for detecting inhibition of a biological assay

A system for detecting inhibition of a biological assay, comprising:

• a detection device configured to amplify and detect a target nucleic acid associated with a target organism during the biological assay, the detection device comprising:
• a reaction chamber configured to receive a sample comprising a matrix and a quantity of the target nucleic acid and to amplify the target nucleic acid within the sample over a nucleic acid amplification cycle;
• a detector, the detector configured to capture, during the nucleic acid amplification cycle, measurements representative of a quantity of the target nucleic acid present in the sample and to store the measurements in a data set;
• a machine learning system configured to receive the data set, wherein the machine-learning system includes processing circuitry trained to detect biological assays inhibited due to matrix inhibition;
• wherein the machine-learning system is further trained to estimate a quantity of the target organism present in the sample based on measurements present in:
• a first data subset, the first data subset including the measurements taken prior to a first point in time, the first point in time occurring prior to a time Tmax, wherein the time Tmax corresponds to a time in the nucleic acid amplification cycle when the measurements reach a maximum amplitude;
• a second data subset, the second data subset including the measurements taken after the first point in time but before a second point in time in the nucleic acid amplification cycle, the second point in time occurring after Tmax;
• a third data subset, the third data subset including the measurements taken after the second point in time in the nucleic acid amplification cycle;
• wherein the nucleic acid cycle has a Tmax;
• wherein a data set is comprised of the first data subset, the second data subset, and/or the third data subset;
• wherein said data set is analyzed based on said trained machine learning system to estimate the quantity of the target organism in the matrix, and/or to indicate whether a threshold amount of the target organism is present;
• and
• in the event that a test is a negative test, said machine learning system is configured to check if said negative test is a false negative.