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AutoSure Voice 3 Blood Glucose Monitoring System:

The AutoSure Voice 3 Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearn, or palm. Testing is done outside the body (In Vitro diagnostic use). The meter includes voice functionality to assist visually impaired users. It is indicated for lay use by people with diabetes as an aid to monitoring levels in Diabetes Mellitus and should only be used by a single patient. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

AutoSure Blood Glucose Test Strips:

The AutoSure Blood Glucose Test Strips are to be used with the AutoSure Voice II and AutoSure Voice 3 Blood Glucose Meters to quantitatively measure glucose in capillary whole Hood taken from fingertips, palm, or forearm. The AutoSure Voice II and AutoSure Voice 3 Blood Glucose Monitoring Systems are plasma-calibrated for easy comparison to lab results. They are intended for self-testing by persons with diabetes and should only be used by a single patient. They are not indicated for the diagnosis or screening of diabetes or for neonatal use.

The AutoSure Voice 3 blood glucose monitoring system consists of the AutoSure Voice 3 meter and AutoSure Test Strips. It is used for testing of blood glucose by self-testers at home.

The provided 510(k) summary for the "AutoSure Voice 3 Blood Glucose Monitoring System" and "AutoSure Blood Glucose Test Strips" does not contain specific acceptance criteria or detailed study results beyond a general statement of "substantial equivalence." It indicates that an "accuracy study" was performed but does not provide the quantitative results, sample sizes, or methodology details required to fully address your request.

Therefore, many sections of your request cannot be fulfilled by the provided document.

Here's a breakdown of what can and cannot be answered:

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

• Acceptance Criteria: Not explicitly stated in the provided text. For blood glucose meters, regulatory bodies typically have established accuracy criteria (e.g., ISO 15197 for in vitro diagnostic test systems). However, these specific criteria are not listed in this submission.
• Reported Device Performance: The document only states that "Results demonstrate substantial equivalence to the predicate device" from an accuracy study. No quantitative performance metrics (e.g., mean absolute relative difference, percentage of results within a certain range of a reference method) are provided.

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

• Sample Size: Not specified for the accuracy study.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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)

• This information is not applicable to a blood glucose monitoring system accuracy study. The "ground truth" (reference value) for blood glucose is typically established using a laboratory-based reference method (e.g., YSI analyzer) operated by trained laboratory professionals, not by medical "experts" in the context of interpretation.

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

• This concept is not applicable to an accuracy study for a blood glucose meter. Adjudication methods are typically used in studies where subjective interpretation is involved (e.g., radiology image reading).

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

• This is not applicable. This device is a blood glucose meter, not an AI-assisted diagnostic tool that involves "human readers" or "AI assistance" in the typical sense of an MRMC study.

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

• This question is less relevant in its typical framing. A blood glucose meter inherently provides a "standalone" reading based on its internal algorithm and sensor. The accuracy study described is essentially evaluating the "standalone" performance of the meter against a reference method. No human "in-the-loop" performance (beyond the user operating the device) would be separately studied in this context.

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

• The ground truth for blood glucose accuracy studies is typically a laboratory-based reference method (e.g., a central laboratory analyzer measuring plasma glucose, often a YSI analyzer). The document states, "The AutoSure Voice II and AutoSure Voice 3 Blood Glucose Monitoring Systems are plasma-calibrated for easy comparison to lab results," implying a comparison to laboratory reference values.

8. The sample size for the training set

• The document does not describe the development or training of an algorithm in a way that would imply a distinct "training set" in the context of machine learning. Blood glucose meters are generally calibrated during manufacturing, and their algorithms are based on electrochemical principles, not typically on machine learning models requiring large "training sets" of patient data.

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

• As above, the concept of a "training set" in the machine learning sense is not applicable based on the provided information for this type of device. Calibration and validation are done against reference methods.

Summary based on available information:

Additional Notes:

The 510(k) summary focuses on demonstrating "substantial equivalence" to a predicate device (AutoSure Voice II meter). This type of submission often omits the granular detail of performance studies that would be required for a novel device, as the primary goal is to show similarity to an already cleared device.

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