Neural network analysis of lfa test strips
AssigneeExa Health, Inc.
InventorsMayank Kumar, Kevin J. Miller, Steven Scherf, Siddarth Satish
Life of this patent
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Abstract
Example methods and systems train an end-to-end neural network machine to analyze images of lateral flow assay test strips by learning non-linear interactions among lighting variations, test strip reflections, bi-directional reflectance distribution functions, angles of imaging, response curves of smartphone cameras, or any suitable combination thereof. Such example methods and systems improve the limit of detection, the limit of quantification, and the coefficient of variation in the precision of quantitative test results, under ambient light settings.
Claims24
A method comprising:
- accessing, by one or more processors, training images that each depict a corresponding test strip of a corresponding test device under a corresponding combination of imaging conditions, the training images being each labeled with a corresponding indicator of a corresponding test result shown by the corresponding test strip;
- training, by the one or more processors, a neural network to determine a predicted test result based on an unlabeled image that depicts a further test strip of a further test device under a corresponding combination of imaging conditions, the training being based on the training images;
- and
- providing, by the one or more processors, the trained neural network to a further machine configured to access the unlabeled image that depicts the further test strip of the further test device under the corresponding combination of imaging conditions and obtain the predicted test result from the trained neural network by inputting the unlabeled image into the trained neural network.
A system comprising:
- one or more processors;
- and
- a memory storing instructions that, when executed by at least one processor among the one or more processors, cause the system to perform operations comprising:
- accessing training images that each depict a corresponding test strip of a corresponding test device under a corresponding combination of imaging conditions, the training images being each labeled with a corresponding indicator of a corresponding test result shown by the corresponding test strip;
- training a neural network to determine a predicted test result based on an unlabeled image that depicts a further test strip of a further test device under a corresponding combination of imaging conditions, the training being based on the training images;
- and
- providing the trained neural network to a further machine configured to access the unlabeled image that depicts the further test strip of the further test device under the corresponding combination of imaging conditions and obtain the predicted test result from the trained neural network by inputting the unlabeled image into the trained neural network.
A machine-readable medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
- accessing training images that each depict a corresponding test strip of a corresponding test device under a corresponding combination of imaging conditions, the training images being each labeled with a corresponding indicator of a corresponding test result shown by the corresponding test strip;
- training a neural network to determine a predicted test result based on an unlabeled image that depicts a further test strip of a further test device under a corresponding combination of imaging conditions, the training being based on the training images;
- and
- providing the trained neural network to a further machine configured to access the unlabeled image that depicts the further test strip of the further test device under the corresponding combination of imaging conditions and obtain the predicted test result from the trained neural network by inputting the unlabeled image into the trained neural network.
Dependencies are inferred from the claim text.