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AutoSure Voice II Plus Blood Glucose Monitoring System:

The AutoSure Voice II Plus Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. 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 and it should not be shared. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

AutoSure Plus Blood Glucose Test Strips:

The AutoSure Plus Blood Glucose Test Strips are to be used with the AutoSure Voice II Plus Blood Glucose Meter to quantitatively measure glucose in capillary whole blood taken from fingertips, palm, or forearm. They are not indicated for the diagnosis or screening of diabetes or for neonatal use.

Contrex Plus III Glucose Control Solutions:

The purpose of the control solution test is to validate the performance of the Blood Glucose Monitoring System using a testing solution with a known range of glucose. A control test that falls within the acceptable range indicates the user's technique is appropriate and the test strip and meter are functioning properly.

The AutoSure Voice II Plus blood glucose meter and AutoSure Plus blood glucose test strips are used for testing of blood glucose by self-testers at home with Contrex Plus III Glucose Control Solutions for quality control testing.

Here's an analysis of the acceptance criteria and study information for the AutoSure Voice II Plus Blood Glucose Monitoring System, based on the provided 510(k) summary:

This device is a blood glucose monitoring system, and as such, its performance is primarily evaluated based on its accuracy in measuring glucose concentrations. The summary describes a "substantial equivalence" determination to a predicate device (AutoSure Voice II Blood Glucose Monitoring System, K102037). While specific numerical acceptance criteria (e.g., percentage within a certain deviation from a reference method) are not explicitly detailed in the provided text, the conclusion of "substantial equivalence" implies that the new device met the expected performance standards for blood glucose meters during non-clinical and clinical testing.

A key aspect for blood glucose meters is meeting ISO standards or FDA guidance for accuracy, which typically specifies a high percentage of results falling within specific error margins when compared to a laboratory reference method. The summary states that "Results demonstrate substantial equivalence to the predicate device meter, test strips, and control solutions" for non-clinical testing and "Results demonstrate substantial equivalence to the predicate system" for clinical testing.

1. Table of Acceptance Criteria and Reported Device Performance

As specific numerical acceptance criteria and precise performance metrics (e.g., specific bias, percentage within error margins) are not explicitly quantified in the provided 510(k) summary for the new device, a direct table of numerical "acceptance criteria" vs. "reported performance" cannot be fully constructed for this specific device.

However, based on the context of blood glucose meter approvals, the implied acceptance criteria would be that the device's performance is comparable to or better than the predicate device and meets established regulatory guidance for accuracy. The reported performance is that this equivalence was met.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: The summary does not explicitly state the sample sizes for the clinical accuracy study (healthcare professionals) or the user performance study (self-testing).
• Data Provenance: The submission originates from China (Taiwan). The studies are described as "Clinical Testing" and "User Performance study," indicating they are likely prospective data collection rather than retrospective, as they were conducted to support the submission of the new device.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

• Number of Experts: The summary mentions "blood testing by healthcare professionals" for the accuracy study. It does not specify the number of individual experts (e.g., laboratory technicians, nurses, or physicians) involved or their specific qualifications (e.g., years of experience as a radiologist). For blood glucose meters, the "ground truth" is typically established by comparative analysis against a laboratory reference method, often performed by trained laboratory personnel.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• The 510(k) summary does not provide any information regarding an adjudication method. For blood glucose meters, the ground truth is typically a direct comparison to a validated central laboratory method, so an adjudication process by multiple clinical experts (as might be seen in imaging studies) is not usually applicable in the same way.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, an MRMC comparative effectiveness study was not done. MRMC studies are typically used for diagnostic imaging devices where human readers interpret and classify images, and the AI's effect on reader performance is evaluated. This device is a blood glucose monitoring system, and its performance is assessed directly against a reference method and through user studies, not through human interpretation of complex data.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, a standalone study was done, essentially. The "Non-Clinical Testing" section describes a series of analytical performance tests (precision, analytical specificity, linearity, etc.) that assess the device's inherent performance characteristics independent of user interaction, comparing it against established benchmarks or the predicate device. The "Clinical Testing" section involving "blood testing by healthcare professionals" also represents a form of standalone performance evaluation, as it assesses the device's accuracy when used by trained individuals, establishing its inherent measurement capability.

7. Type of Ground Truth Used

• The ground truth for both non-clinical and clinical testing would have been established by laboratory reference methods for glucose measurement. For example, a YSI reference method is commonly used to determine the true glucose concentration in blood samples. The device's readings are then compared to these reference values.

8. Sample Size for the Training Set

• Not Applicable / Not Provided. This device is a blood glucose meter, not a machine learning or AI-driven diagnostic imaging system that typically has a separate "training set" for model development. The device's calibration and analytical performance are based on its electrochemical design and manufacturing specifications, which are established through a different development and validation process than algorithmic training. The 510(k) summary does not mention any machine learning components that would require a "training set."

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. As explained above, there is no mention or implication of a machine learning component requiring a "training set" in the context of this traditional medical device.

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