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The Gmate® VOICE Blood Glucose Monitoring System is intended for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, hand, upper arm, forearm, calf or thigh as an aid in monitoring the effectiveness of diabetes management in the home by individuals with diabetes. The Gmate® VOICE Blood Glucose Monitoring System is intended to be used by a single user and should not be shared with any other person.

The Gmate® VOICE Blood Glucose Monitoring System is for self-testing outside the body (in vitro diagnostic use only) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The Gmate® VOICE Blood Glucose Monitoring System should not be used for the diagnosis or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The Gmate® VOICE Blood Glucose Monitoring System includes a speaking feature that provides audible test results for diabetic users.

The Gmate® Blood Glucose Test Strips are for use with the Gmate® VOICE Meter for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, hand, upper arm, forearm, calf or thigh.

The Gmate® Control Solution is for use with the Gmate® VOICE Blood Glucose Monitoring System and is intended as a quality control measure to verify the accuracy of your blood glucose test results and to ensure that the Gmate® VOICE meter and Gmate® Test Strips are working properly. The Gmate® Control Solution is intended for use by people with diabetes at home.

The Gmate® VOICE Blood Glucose Monitoring System is an in vitro diagnostic device designed for measuring the concentration of glucose in whole blood, which is used with the Gmate® Blood Glucose Test Strips.

The test principle is:

This device is an in vitro diagnostic product intended for the measurement of glucose concentration in human blood. The principle of the test relies upon a specific type of glucose in the blood sample, the glucose oxidase that reacts to electrodes in the test strip. The test strip employs an electrochemical signal generating an electrical current that will stimulate a chemical reaction. This reaction is measured by the Meter and displayed as your blood glucose result.

This document is a 510(k) summary for the Gmate® VOICE Blood Glucose Monitoring System, which primarily focuses on establishing substantial equivalence to a predicate device rather than detailing specific clinical study results against acceptance criteria. Therefore, much of the requested information regarding acceptance criteria and study particulars for a medical AI device is not present. However, I can extract the available relevant information and highlight the missing details based on the context of a 510(k) submission.

Missing Information:
This document is a 510(k) summary for a blood glucose monitoring system, not specifically an AI-powered diagnostic device in the modern sense. Consequently, several of the requested categories are not applicable or not detailed in this type of regulatory submission. Specifically, there is no mention of "AI" or "machine learning," and therefore no information on:

• Multi-reader multi-case (MRMC) comparative effectiveness study
• Effect size of human readers with vs. without AI assistance
• Standalone algorithm performance
• Training set sample size
• How ground truth for the training set was established

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

The document does not explicitly state "acceptance criteria" in the format typically seen for an AI diagnostic device. However, regulatory submissions like this often implicitly adhere to performance benchmarks for blood glucose meters. The study described is an "accuracy study" that aims to compare the device's performance against a reference method and implicitly against established standards for blood glucose meters.

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 this 510(k) summary.
• Data Provenance: Not specified in this 510(k) summary. The company has offices in New York and Seoul, but the origin of the study data is not stated. The studies would typically be prospective to evaluate a new device.

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 is not applicable as the device measures blood glucose which is typically compared against a laboratory reference method, not expert-established ground truth.

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

Not applicable for a blood glucose monitoring system that compares readings to a reference lab method.

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

Not applicable. This is a blood glucose meter, not an AI-assisted diagnostic tool requiring human reader interpretation. No AI component is mentioned.

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

This device operates as a standalone blood glucose meter. The "algorithm" is the electrochemical detection and glucose concentration calculation. The performance described in the underlying studies (not detailed in this document) would be of the device in its entirety without human-in-the-loop performance modifications, other than the user performing the test.

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

For blood glucose monitoring systems, the ground truth is typically established by laboratory reference methods (e.g., YSI glucose analyzer) which are considered highly accurate and precise.

8. The sample size for the training set

Not applicable or not specified. This is a traditional medical device, not an AI model requiring a distinct "training set" in the machine learning sense. The device is calibrated and validated through internal testing before market submission.

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

Not applicable or not specified, for the same reasons as #8. Ground truth for calibration and validation would be established via highly accurate laboratory reference methods.

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