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The EZ Smart Blood Glucose Test Strips are uses with the EZ Smart Meter to measure Glucose (sugar) in whole blood. The EZ Smart Test strips are for testing outside the body (in vitro diagnostic use ). The EZ Smart Blood Glucose Monitoring System is intended for use in the home and in the professional settings to monitor blood glucose levels for better glucose level control among diabetics.

The EZ Smart Blood Glucose Test Strips are used with the EZ Smart Blood Glucose Meter to measure glucose (sugar) in whole blood. The EZ Smart Test Strips are for testing outside the body (in vitro diagnostic use). The EZ Smart Blood Glucose Monitoring System is intended for use in the home and in professional settings to monitor blood glucose levels. It is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The document does not contain explicit acceptance criteria in the format of a table with specific numerical targets for accuracy, precision, or other performance metrics. This is often the case for 510(k) submissions where substantial equivalence is demonstrated against a predicate device rather than meeting pre-defined, quantitative acceptance criteria established by a standard or regulatory body for a novel device.

Instead, the document states:
"An evaluation of the EZ Smart was conducted under various conditions including temperature effects, hematocrit levels, sensitivity and linearity, and presence of interferences. The results of the evaluation demonstrate that the EZ Smart is equivalent in performance to the predicate device and suitable for its intended use."

This indicates that the "acceptance criteria" were implied by demonstrating performance "equivalent" to the predicate device (Bayer Elite with Elite Test Strips, K964630 for the meter and K991242 for the test strips). The specific numerical results of these evaluations (e.g., accuracy percentages, bias) are not provided in the submitted text.

Here's a breakdown of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified in the provided text.
• Data Provenance: Not specified in the provided text.
• Retrospective or Prospective: Not specified in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable/Not specified. For blood glucose monitoring systems, ground truth is typically established by laboratory reference methods, not by expert consensus in the same way it would be for image interpretation. The document does not mention human experts establishing ground truth.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable/Not specified. Adjudication methods are typically used for subjective assessments or when multiple human readers interpret data, which is not the primary method for evaluating blood glucose meters.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No MRMC or AI comparative effectiveness study was mentioned. The device is a standalone blood glucose monitoring system, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

• Yes, performance evaluations of the EZ Smart Blood Glucose Monitoring System were conducted as a standalone device. The description of "various conditions" (temperature, hematocrit, sensitivity, linearity, interferences) implies direct testing of the device's measurement capabilities.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• While not explicitly stated, for blood glucose monitoring systems, ground truth is typically established using laboratory reference methods (e.g., YSI analyzer) which are considered highly accurate for glucose measurement.

8. The sample size for the training set

• Not applicable/Not specified. This is a measurement device, not an AI/machine learning model that requires a distinct "training set." Any internal calibration or development data are not described.

9. How the ground truth for the training set was established

• Not applicable/Not specified. As it's not an AI/ML device, the concept of a "training set" and its associated ground truth establishment is not relevant in the context of this 510(k) summary.

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