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For in vitro diagnostic use (i.e. for external use only) by healthcare professionals and in the home by people with diabetes mellitus to assess the performance of the Roche Accu-chek Active, the Bayer Ascensia Microfill, and the LifeScan OneTouch Ultra and FastTake Blood Glucose Monitors.

The Liberty Normal Glucose Control consists of a viscosity-adjusted, aqueous liquid control solution containing a known quantity of glucose. The product is packaged in plastic dropper tipped bottles for easy application of the control solutions to the test strips and a red coloration to aid the user to visually confirm application of the control. The product is non-hazardous and contains no human or animal derived materials.

The provided document describes a Quality Control Material for blood glucose monitors, not an AI/ML-powered diagnostic device. Therefore, many of the requested categories for AI/ML device studies (such as sample size for test sets, data provenance, number of experts, adjudication methods, MRMC studies, standalone performance, training set data, etc.) are not applicable.

The document focuses on demonstrating substantial equivalence to existing predicate devices based on comparable characteristics and performance studies relevant to control solutions.

Here's a breakdown of the available information relevant to acceptance criteria and performance, alongside an explanation of why certain AI/ML-specific questions cannot be answered:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of Applicability/Non-Applicability to AI/ML Questions:

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

  • Not applicable for a control solution. This device is a consumable quality control material, not a diagnostic algorithm. Performance is assessed through chemical stability and precision testing, not diagnostic accuracy on a patient dataset. The "test set" here would refer to vials of the control solution and measurements taken by various blood glucose meters.

• 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. Ground truth for a glucose control solution is established by precise chemical formulation and analytical testing, not by expert interpretation of medical images or patient data.

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

  • Not applicable. Adjudication methods are relevant for ambiguous diagnostic interpretations, not for the chemical properties and performance of a control solution.

• 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 pertains to AI-assisted diagnostic reads, which is not what this device is.

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

  • Not applicable. This device is a control solution, not an algorithm.

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

  • Ground truth: For a glucose control solution, the ground truth is the known, precisely manufactured concentration of glucose within the solution, verified through analytical chemistry methods.

• 8. The sample size for the training set:

  • Not applicable. This device is not an AI/ML algorithm that requires a training set. Its formulation is based on chemical engineering and existing predicate control solutions.

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

  • Not applicable. As above, no training set for this type of device.

Conclusion:

The Liberty Normal Glucose Control is a chemical control solution designed to verify the performance of blood glucose monitors. Its acceptance criteria and performance are established through demonstrating substantial equivalence to legally marketed predicate devices by comparing their physical characteristics, chemical matrix, intended use, and through standard performance studies for such products (stability, open vial, microbial stress, and test precision). The 510(k) summary indicates that these comparisons and studies support the claim of substantial equivalence. The document is not for an AI/ML diagnostic device, so questions related to AI/ML specific study designs are not relevant.

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The Ascensia® CONTOUR® Blood Glucose Monitoring System is used for the measurement of glucose in whole blood. The Ascensia® CONTOUR® Blood Glucose Monitoring System is an over-the-counter (OTC) device used by persons with diabetes and by healthcare professionals in home settings and in healthcare facilities.

The Ascensia ® CONTOUR® Blood Glucose Monitoring System is indicated for use with capillary, venous, and arterial whole blood samples. Capillary samples may be drawn from the fingertip, palm, forearm, abdomen and thigh.

The frequent monitoring of blood glucose is an adjunct to the care of persons with diabetes.

The Ascensia® CONTOUR® Blood Glucose Monitoring System (modified test strip) is used for the measurement of glucose in whole blood. The strip is one component of a system that also contains a meter, controls, lancing device, and instructions for use.

The provided document is a 510(k) summary for the Ascensia® CONTOUR® Blood Glucose Monitoring System (modified test strip). It compares the modified device to a predicate device and assesses its performance. However, it does not explicitly state specific acceptance criteria (e.g., in terms of mean absolute relative difference or error grids) with defined numerical thresholds. Instead, the assessment is based on demonstrating "equivalent performance" and "substantial equivalence" to the predicate device and a laboratory method.

Here's an attempt to structure the information based on the request, inferring what can be gathered from the provided text:

Acceptance Criteria and Device Performance

Detailed Study Information:

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
   The document states: "An evaluation of the Ascensia® CONTOUR® Blood Glucose Monitoring System (modified test strip) was studied in the laboratory and in a clinical setting by persons with diabetes."

   • Sample Size: The exact sample size for the test set (number of patients or measurements) is not specified in the provided text.
   • Data Provenance: The country of origin of the data is not specified. The study involved both "laboratory" and "clinical setting," and was conducted by "persons with diabetes," implying real-world usage, but the prospective/retrospective nature is not explicitly stated. It can be inferred as prospective for the clinical setting part.

2. 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):
   The ground truth was established by comparison to "a laboratory method" and the "original Ascensia® CONTOUR® Blood Glucose Monitoring System."

   • Number of Experts: The document does not specify the number of experts.
   • Qualifications of Experts: The qualifications of individuals performing the "laboratory method" are not specified.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
   The document does not describe an adjudication method as it relates to expert consensus or discrepancies. The comparison was against a laboratory method and a predicate device.

4. 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 device is a blood glucose monitoring system, not an AI-assisted diagnostic imaging device for human readers. Therefore, an MRMC comparative effectiveness study is not applicable in this context, and no information on human reader improvement with AI is provided.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   This question is related to AI/algorithm performance. The device is a "Blood Glucose Monitoring System," which inherently involves a measurement system (modified test strip and meter). The evaluation focused on the performance of this system itself, comparing its measurements to existing methods. It's a standalone device in the sense that the measurement output is directly compared, rather than being an algorithm that assists a human.
   However, if "standalone" refers to an algorithm without human intervention in interpreting the output, then the device itself is standalone in producing a glucose reading. The performance assessment was of the "Ascensia® CONTOUR® Blood Glucose Monitoring System (modified test strip)" itself.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   The ground truth was established by:

   • Comparison to results from the "original Ascensia® CONTOUR® Blood Glucose Monitoring System" (the predicate device).
   • Comparison to "a laboratory method." This "laboratory method" would typically be a highly accurate and precise reference method for blood glucose measurement (e.g., YSI analyzer, hexokinase method), often considered the gold standard for glucose concentration.

7. The sample size for the training set:
   The document describes an "evaluation" or "study" of the modified device. It does not mention a "training set" as would be relevant for machine learning algorithms. This device is a biochemical measurement system, not an AI/ML model that requires training.

8. How the ground truth for the training set was established:
   As there is no mention of a "training set" for an AI/ML model, this question is not applicable.

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