Analyzing and recording device quality
A system including a processor and memory coupled to the processor. A communications interface is coupled to the processor for communicating with a central test database. A carrier is provided for receiving a cassette containing a lateral flow assay (“LFA”) strip. An LFA strip reader is coupled to the processor for taking an image of the LFA strip and cassette and transmitting the image to the processor. The processor analyzes the image to determine a vendor of the cassette, analyzes the image of the LFA strip using a machine learning engine for quality defects in the LFA strip, generates an error when a quality defect is detected in the LFA strip, and reports the error to the central test database.
1. A system, comprising:
a processor;
memory coupled to the processor;
a communications interface coupled to the processor for communicating with a central test database;
a carrier for receiving a cassette containing a lateral flow assay (“LFA”) strip;
an LFA strip reader coupled to the processor for taking an image of the LFA strip and cassette and transmitting the image to the processor, wherein the processor analyzes the image to determine a vendor of the cassette, analyzes the image of the LFA strip using a machine learning engine for quality defects in the LFA strip, generates an error when a quality defect is detected in the LFA strip, and reports the error to the central test database.
2. The system of claim 1 , further comprising an environmental sensor for sensing environmental data, the environmental sensor in communication with the processor to send the environmental data to the processor.
3. The system of claim 2 wherein the environmental sensor is selected from a group consisting of a temperature sensor and a humidity sensor.
4. The system of claim 2 , wherein reporting the error further comprises sending the environmental data to the central test database for storage.
5. The system of claim 1 wherein reporting the error comprises sending the image of the LFA strip and cassette along with an identity of the vendor to the central test database for storage.
6. The system of claim 1 wherein quality defect is one or more of a break in the LFA strip, dust, debris on the LFA strip, plastic defects, and defects in a visual feature on the carrier.
7. The system of claim 1 , wherein the processor is further operable to download the machine learning engine over the communication interface.
8. The system of claim 1 , further comprising a patient identification reader in communication with the processor, the patient identification reader operable to read patient identification information and provide the patient identification information to the processor and the processor further operable to associate the patient identification information with a unique cassette identifier determined from a visual feature on the carrier.
9. A method comprising:
receiving a cassette containing a lateral flow assay (“LFA”) strip at a carrier;
taking an image of the LFA strip and cassette;
transmitting the image to a processor;
analyzing the image at the processor to determine a vendor of the cassette and to identify quality defects in the LFA strip, using a machine learning engine;
generating an error when a quality defect is detected in the LFA strip; and
reporting the error to a central test database, such that a result of the LFA strip with the quality defect is marked as invalid.
10. The method of claim 9 , further comprising:
sensing environmental data at an environmental sensor, sending the environmental data to the processor.
11. The method of claim 10 wherein the environmental sensor is selected from a group consisting of a temperature sensor and a humidity sensor.
12. The method of claim 10 , wherein reporting the error further comprises sending the environmental data to the central test database for storage.
13. The method of claim 9 , wherein reporting the error comprises sending the image of the LFA strip and cassette along with an identity of the vendor to the central test database for storage.
14. The method of claim 9 , wherein quality defect is one or more of a break in the LFA strip, dust, debris on the LFA strip, plastic defects, and defects in a visual feature on the carrier.
15. The method of claim 9 , further comprising:
downloading the machine learning engine by the processor to a local memory a communication interface.
16. The method of claim 9 , further comprising:
reading patient identification information from the cassette, and providing the patient identification to the processor; and
associating the patient identification information with a unique cassette identifier determined from a visual feature on the carrier.
17. A system comprising:
a carrier for receiving a cassette containing a lateral flow assay (“LFA”) strip;
an LFA strip reader coupled to a processor for taking an image of the LFA strip and cassette and transmitting the image to the processor; and
the processor to analyze the image of the LFA strip using a machine learning engine for quality defects in the LFA strip and generate an error when a quality defect is detected in the LFA strip and report the error to a central test database.
18. The system of claim 17 , wherein quality defect is one or more of a break in the LFA strip, dust, debris on the LFA strip, plastic defects, and defects in a visual feature on the carrier.
19. The system of claim 17 , wherein reporting the error comprises sending the image of the LFA strip and cassette along with an identity of a vendor to the central test database for storage.
20. The system of claim 17 , further comprising:
an environmental sensor selected from a group consisting of a temperature sensor and a humidity sensor for sensing environmental data, the environmental sensor to send the environmental data to the processor; and
wherein reporting the error further comprises including the environmental data with the quality defect in the central test database.