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US 2023/0416801 A1Published application
Antimicrobic susceptibility testing using recurrent neural networks
AssigneeBeckman Coulter, Inc.
InventorFrederick Rupisan CUENCO
Life of this patent
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Abstract
An optimized testing method is used to determine minimum inhibitory concentration (MIC) of a particular antimicrobic for use on a sample. This may include iteratively imaging wells inoculated with the sample and containing various concentrations of the antimicrobic. The images are thereafter processed to identify MIC based on sequences in information provided as input to a machine learning model.
Claims20
A method comprising:
- (a) creating a plurality of test mixtures in a plurality of test wells, wherein:
- (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample;
- (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent;
- (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures;
- and
- (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures;
- (b) incubating each of the test mixtures;
- (c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture;
- (d) obtaining a plurality of growth predictions by performing steps comprising, for each test mixture from the plurality of test mixtures, providing a data sequence to a machine learning model, wherein:
- (i) the data sequence comprises a plurality of input items;
- (ii) each input item from the plurality of input items corresponds to an imaging time from the plurality of imaging times;
- and
- (iii) the machine learning model is adapted to recognize, and to make growth predictions based on, temporal sequences;
- and
- (e) generating a minimum inhibitory concentration, MIC, determination for the biological sample based on the plurality of growth predictions.
12 more elements
A biological testing system comprising a processor configured with a set of computer instructions operable, when executed, to cause the system to perform a method comprising:
- (a) creating a plurality of test mixtures in a plurality of test wells, wherein:
- (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample;
- (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent;
- (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures;
- and
- (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures;
- (b) incubating each of the test mixtures;
- (c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture;
- (d) obtaining a plurality of growth predictions by performing steps comprising, for each test mixture from the plurality of test mixtures, providing a data sequence to a machine learning model, wherein:
- (i) the data sequence comprises a plurality of input items;
- (ii) each input item from the plurality of input items corresponds to an imaging time from the plurality of imaging times;
- and
- (iii) the machine learning model is adapted to recognize, and to make growth predictions based on, temporal sequences;
- and
- (e) generating a minimum inhibitory concentration, MIC, determination for the biological sample based on the plurality of growth predictions.
12 more elements
A computer program product comprising a non-transitory computer readable medium having stored thereon a set of computer instructions operable, when executed, to cause a biological testing system to perform a method comprising:
- (a) creating a plurality of test mixtures in a plurality of test wells, wherein:
- (i) each test mixture from the plurality of test mixtures is inoculated using a biological sample;
- (ii) each test mixture from the plurality of test mixtures comprises an antimicrobial solution comprising an antimicrobial agent;
- (iii) in each test mixture from the plurality of test mixtures, the antimicrobial solution in that test mixture differs from the antimicrobial solution in each other test mixture from the plurality of test mixtures;
- and
- (iv) the same biological sample is used to inoculate each test mixture from the plurality of test mixtures;
- (b) incubating each of the test mixtures;
- (c) for each test mixture from the plurality of test mixtures, at a plurality of imaging times, wherein each of the imaging times takes place after incubation has begun, capturing an image of that test mixture;
- (d) obtaining a plurality of growth predictions by performing steps comprising, for each test mixture from the plurality of test mixtures, providing a data sequence to a machine learning model, wherein:
- (i) the data sequence comprises a plurality of input items;
- (ii) each input item from the plurality of input items corresponds to an imaging time from the plurality of imaging times;
- and
- (iii) the machine learning model is adapted to recognize, and to make growth predictions based on, temporal sequences;
- and
- (e) generating a minimum inhibitory concentration, MIC, determination for the biological sample based on the plurality of growth predictions.
12 more elements
Dependencies are inferred from the claim text.