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The BGM009 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. Alternative site testing should be performed only during steady-state (when glucose is not changing rapidly). It is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid in monitoring the effectiveness of diabetes control 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.
The BGM009 Blood Glucose Test Strips are to be used with the BGM009 and BGM009 Plus Blood Glucose Meter to quantitatively measure glucose in capillary whole blood taken from fingertips, palm, or forearm.

The BGM009 Plus blood glucose monitoring system consists of the BGM009 Plus meter and BGM009 Test Strips. It is used for testing of blood glucose by self-testers at home.

The provided document does not contain the detailed information necessary to answer all parts of your request. It is a 510(k) summary for a Blood Glucose Monitoring System where the applicant is asserting substantial equivalence to a predicate device.

Here's what can be extracted and what information is missing:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state acceptance criteria or provide a detailed table of device performance against specific metrics for the BGM009 Plus Blood Glucose Monitoring System. It only states that "Non-clinical testing show that the BGM009 Plus meter with the BGM009 Strips perform in a substantially equivalent manner to that of the predicate device."

2. Sample sized used for the test set and the data provenance

This information is not provided in the document. The document mentions "Software verification and validation were done" and "Non-clinical testing," but no details on sample size or data provenance (e.g., country of origin, retrospective/prospective) are given for these tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided in the document. As this is a blood glucose monitoring system, the ground truth would likely refer to a laboratory reference method, not expert interpretation.

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

This information is not provided in the document.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is typically relevant for interpretative devices where human readers (e.g., radiologists) interact with AI. This document pertains to a blood glucose monitoring system, which is a quantitative measurement device. Therefore, an MRMC study as described would not be applicable and is not mentioned. The device does not involve human "readers" or AI assistance in the way described for an MRMC study.

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

The document states "Clinical Testing: N/A". This suggests that no standalone clinical performance study was conducted specifically for the BGM009 Plus device in the context of this 510(k). The focus is on non-clinical testing demonstrating equivalence to the predicate. The "algorithm" here is the blood glucose measurement technology, and its standalone performance would typically be confirmed through accuracy studies against a reference method, which are usually considered "clinical testing" in this context.

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

While not explicitly stated for the BGM009 Plus, for blood glucose monitoring systems, the ground truth for accuracy studies is typically established using a laboratory reference method (e.g., YSI analyzer) to measure glucose concentration in blood samples. This is implied by the nature of the device.

8. The sample size for the training set

This information is not provided in the document. While the device utilizes an "algorithm," the document does not discuss machine learning or AI training sets. The "algorithm" likely refers to the electrochemical principles and calculations used to determine glucose concentration.

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

This information is not provided in the document, as the concept of "training set" in the context of machine learning/AI is likely not applicable or discussed for this type of medical device submission.

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MEG-2B Blood Glucose Monitoring System: The MEG-2B Blood Glucose Monitoring System, includes MEG-2B Blood Glucose Meter and MEG-2B Blood Glucose Test Strip, is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Alternate site testing should be done only during steady -state times (when glucose is not changing rapidly). It is indicated for use by lay people with diabetes at home as an aid to monitor the effectiveness of diabetes control. This system is intended for self-testing outside the body (in vitro diagnostic use) and should not be shared. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

MEG-2B Pro Blood Glucose Monitoring System: The MEG-2B Pro Blood Glucose Monitoring System includes MEG-2B Pro Blood Glucose Meter and MEG-2B Pro Blood Glucose Test Strip, is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Alternate site testing should be done only during steady -state times (when glucose is not changing rapidly). This system is indicated for multiple-patient use in professional healthcare settings as an aid to monitor the effectiveness of diabetes control programs. It is only used with single-use, auto-disabling lancing device. It is intended for testing outside the body (in vitro diagnostic use). It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

The MEG-2B Blood Glucose Monitoring System consists of the MEG-2B meter and MEG-2B Test Strips. It is used for testing of blood glucose by self-testers at home. The MEG-2B Pro Blood Glucose Monitoring System consists of the MEG-2B Pro meter and MEG-2B Pro Test Strips. It is used for testing of blood glucose by professional testers in healthcare facilities. The MEG-2B and MEG-2B Pro systems are identical other than trade names and details of product labeling.

This document is a 510(k) premarket notification for a blood glucose monitoring system, and as such, it does not contain the detailed information about acceptance criteria and a study design that you would typically find for an AI/ML-based medical device. The device in question is a physical blood glucose meter, and the changes discussed are related to manufacturing and disinfectants, not a software algorithm or AI.

Therefore, I cannot extract the information requested for the following reasons:

• No Acceptance Criteria for AI/ML performance: The document describes a blood glucose monitoring system, not an AI/ML device. Acceptance criteria discussed would relate to accuracy of glucose measurements, not AI performance metrics like sensitivity, specificity, or AUC.
• No "Study" as defined for AI/ML: The "studies" mentioned are "Disinfection (viral inactivation) and 'robustness' testing," which are physical/chemical engineering tests, not clinical studies for AI/ML performance. It explicitly states, "No clinical testing was conducted."
• No data on training/test sets, ground truth, experts, or MRMC studies: These concepts are entirely irrelevant to the type of device and the submission described.

The prompt specifically asks for details related to an AI/ML device's validation. This document is about a different class of medical device (in vitro diagnostic glucose meter) and its physical modifications.

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The AutoSure Voice 3 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.

The AutoSure Plus Blood Glucose Test Strips are to be used with the AutoSure Voice 3 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.

The AutoSure Voice 3 Plus blood glucose meter and AutoSure Plus test strips are used for testing of blood glucose by self-testers at home. Contrex Plus III Glucose Control Solutions are used for quality control testing of the system.

The provided text is a 510(k) Summary for the AutoSure Voice 3 Plus Blood Glucose Monitoring System. It details the device, its intended use, and a comparison to a predicate device. However, it does not contain specific acceptance criteria, detailed clinical study results with performance metrics, sample sizes for test sets, data provenance, expert qualifications, adjudication methods, or information about MRMC studies, standalone performance, training sets, or how ground truth was established.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (AutoSure Voice II Plus Blood Glucose Monitoring System) based on changes in button relocation and the use of the same test algorithm and test strips.

Here's an analysis based only on the provided text, highlighting what is present and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

The document explicitly states that the AutoSure Voice 3 Plus system uses the same test algorithm and test strips as the predicate meter (AutoSure Voice II Plus Blood Glucose Monitoring System). This implies that the performance expectations align with those of the predicate device. However, the specific acceptance criteria and numerical performance metrics for either the predicate or the new device are not provided in this summary.

The document mentions:

• "EMC & Electrical Safety and linearity testing. Results demonstrate substantial equivalence to the predicate system."
• "A user survey shows substantial equivalence in ease-of-use after the relocation of the operation buttons."
• "Testing demonstrated that the AutoSure Voice 3 Plus system performs in a substantially equivalent manner to that of the predicate."

Therefore, a table cannot be constructed with specific metrics based on the provided text. The general "acceptance criterion" appears to be "substantial equivalence" to the predicate device.

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

• Test Set Sample Size: Not specified. The document mentions a "user survey" for ease-of-use but does not provide details on the number of participants.
• Data Provenance (Country of Origin, Retrospective/Prospective): Not specified. The submitter is from China (Taiwan), but the location of the clinical testing (user survey) is not mentioned. The nature of the user survey (retrospective or prospective) is also not specified.

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

• Not Applicable / Not Specified. For a blood glucose monitoring system, the "ground truth" for glucose levels is typically established by a laboratory reference method, not by human experts interpreting data or images. The document does not describe any expert involvement in establishing ground truth for glucose measurements or for the user survey.

4. Adjudication Method for the Test Set:

• Not Applicable / Not Specified. As there's no mention of expert ground truth establishment, there's no adjudication method described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size:

• No. An MRMC study is typically performed for diagnostic imaging devices where multiple readers interpret cases. This device is a blood glucose monitoring system, which does not involve human readers interpreting images.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

• Yes, implicitly. The device itself is designed to provide a quantitative measurement of glucose. The "test algorithm" is key to this standalone performance. The document states that the new device uses the "same test algorithm" as the predicate. The "EMC & Electrical Safety and linearity testing" would also fall under standalone performance testing. However, specific performance results (e.g., accuracy, precision) of this standalone algorithm are not provided in this summary.

7. The Type of Ground Truth Used:

• Not explicitly stated in detail, but for a blood glucose monitoring system, the ground truth for glucose measurements would typically be established by a laboratory reference method (e.g., a YSI analyzer or similar highly accurate laboratory instrument). The summary does not provide details on the reference method used or how it was applied during any performance studies. For the "user survey," the ground truth was "user satisfaction/ease-of-use."

8. The Sample Size for the Training Set:

• Not Applicable / Not Specified. For a traditional blood glucose meter, there isn't typically a "training set" in the sense of machine learning. The algorithm is based on electrochemical principles and calibration.

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

• Not Applicable. As there's no distinct training set mentioned in the machine learning context, this question isn't relevant to the information provided.

In summary, the provided 510(k) Summary focuses on demonstrating substantial equivalence primarily through design changes (button relocation) and the continuity of the core technology (same algorithm and test strips). It lacks granular detail on performance criteria, clinical study methodology, and specific results often found in submissions for devices with more complex diagnostic algorithms or imaging components.

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The purpose of the control solution test is to validate the performance of the GlucoSure STAR & GlucoTRACK Blood Glucose Monitoring Systems 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.

Control solution containing D-glucose as its reactive component for use as a quality control material for blood glucose monitoring systems.

The provided text describes the acceptance criteria and the study conducted for the Contrex Plus Low Control Solution. This device is a quality control material for blood glucose monitoring systems, not an AI-powered diagnostic device, so many of the requested AI/MRMC specific questions are not applicable.

Here's the information based on the provided text, with "N/A" (Not Applicable) for questions that don't fit the scope of this type of device or for which information is not provided.

Acceptance Criteria and Device Performance

Study Details

1. A table of acceptance criteria and the reported device performance: (See table above)

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

   • Control Solution Qualification: 80 test strips over 10 days (for one lot of control solution).
   • Use Life Study: 5 meters, one lot of test strips. Three lots of low control solution were tested, with 8 bottles per lot (4 for open-bottle testing, 4 for un-opened vial testing).
   • Shelf Life Study: Two lots of test strips, ten meters. Three lots of low control solution were tested. For each lot, 10 test strips were tested at each test point.
   • Data Provenance: Not explicitly stated, but the submitter is Apex BioTechnology Corp. from Hsinchu Science Park, CHINA (TAIWAN). The studies appear to be prospective, laboratory-based testing for product quality control.

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):

   • N/A. This is a quality control material where "ground truth" is defined by the expected chemical concentration range of glucose in the solution, and its interaction with a device. No human expert interpretation is involved in establishing the "ground truth" of the control solution's performance.

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

   • N/A. Adjudication methods are typically relevant for diagnostic or image-based studies where expert consensus resolves discrepancies. For a control solution, the results are quantitative measurements against predefined ranges.

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:

   • N/A. This is not an AI/imaging device.

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

   • N/A. This is a control solution for a blood glucose monitoring system, not an algorithm. The "performance" is the accuracy of the glucose meter reading the control solution within its specified range.

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

   • The "ground truth" for the control solution's performance is the assigned control solution range (40-70 mg/dL), which is presumably established through rigorous chemical analysis during the manufacturing process of the control solution itself. The conformity of the meter's reading to this known chemical range is the measure of the system's performance.

8. The sample size for the training set:

   • N/A. This is not an AI/machine learning device that requires a training set.

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

   • N/A. No training set is applicable.

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