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The ACCU-CHEK Nano SmartView Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips or palm. The ACCU-CHEK Nano SmartView Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

The ACCU-CHEK Nano SmartView Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The ACCU-CHEK Nano Smartview Blood Glucose Monitoring System should not be used for the diagnosis of 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 ACCU-CHEK* SmartView Test Strips are for use with the ACCU-CHEK Nano Blood Glucose Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips or palm.

The ACCU-CHEK® Nano meter was developed to utilize the ACCU-CHEK® Aviva Plus test system's technology and performance characteristics. The ACCU-CHEK® Nano meter designers took the measurement components of the ACCU-CHEK® Aviva Plus system, slightly changed the firmware and hardware supporting the new user interface and housing and embedded/programmed the strip lot code information within the meter so a physical code key or code key port are no longer used.

The ACCU-CHEK ® SmartView test strip is a No Code Freedom 2 Chemistry test strip which shares the same scientific technology as the predicate device, the ACCU-CHEK® Aviva Plus test strips. The instrument's measurement method is not modified as a part of this test strip modification project.

When an ACCU-CHEK® SmartView test strip is inserted into the ACCU-CHEK® Nano meter, a small alternating current (AC) is applied until the application of blood causes a spike in the conductivity to be observed at the measurement and sample-sufficiency electrodes – both are used to assure an adequate sample has been applied.

The instrument then applies a series of AC voltages at four frequencies and reads the AC responses. These carry information about the sample type and environmental temperature; they also allow the system to perform various internal quality checks.

After the AC measures are completed, a small (DC) voltage is applied and current is observed which is proportionate to the glucose. The AC and DC information are then combined to provide a hematocrit and temperature compensated glucose result.

The single-patient use ACCU-CHEK® Nano SmartView Blood Glucose Monitoring System will consist of:

Meter: ACCU-CHEK® Nano Meter Test Strip: ACCU-CHEK® SmartView Test Strip Controls: ACCU-CHEK® SmartView Control Solutions

The provided text describes the ACCU-CHEK Nano SmartView Blood Glucose Monitoring System and its substantial equivalence to a predicate device. However, it does not explicitly detail the acceptance criteria for performance, nor does it present the results of a specific study in the format requested. The document primarily focuses on the device's description, intended use, and a statement of substantial equivalence based on "performance testing."

Therefore, I cannot provide a table of acceptance criteria and reported device performance, information on sample sizes for a test set, ground truth establishment methods, expert qualifications, adjudication methods, or details about MRMC or standalone studies with specific effect sizes from the provided text.

The text does state: "Performance testing on the ACCU-CHEK® Nano SmartView Blood Glucose Monitoring System demonstrated that the device meets the performance requirements for its intended use. The data demonstrates that the test strip is substantially equivalent to the predicate device." This indicates that such testing was conducted and met predefined criteria, but the specific details are not included in this summary.

Based on the provided input, the following information can be extracted:

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

   • Not explicitly provided in the text. The document states that performance testing demonstrated that the device meets the performance requirements for its intended use and that the test strip is substantially equivalent to the predicate device. Specific numerical acceptance criteria and reported performance values are not given.

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

   • Not explicitly provided in the text. The document mentions "Performance testing," but does not specify the sample size or data provenance (e.g., country of origin, retrospective/prospective nature).

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/provided in the text. For a blood glucose monitoring system, the "ground truth" is typically established by comparative measurements against a well-established laboratory reference method, not through expert consensus in the way it would be for image interpretation by clinicians. The text does not detail the specific ground truth method used for performance testing or the involvement of human "experts" in establishing it beyond the use of a reference method.

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

   • Not applicable/provided in the text. Adjudication methods are typically used when multiple human readers interpret data, and their findings need to be reconciled to establish a ground truth or resolve discrepancies. This is not directly relevant to the performance testing of a blood glucose monitoring system, which typically involves comparison to a reference 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:

   • No, an MRMC study was not done (and is not applicable). This device is a blood glucose monitoring system, not an AI-assisted diagnostic tool for human readers. Its purpose is direct measurement.

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

   • Yes, effectively. The performance testing described (though details are absent) would inherently be a standalone evaluation of the device's accuracy in measuring glucose levels against a reference. The device itself is an "algorithm only" type of system in that it measures and reports a value without human interpretation in the loop of the measurement process.

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

   • Implied to be a laboratory reference method. For blood glucose monitoring systems, ground truth is typically established by comparing the device's readings against results from a highly accurate laboratory analyzer (e.g., YSI analyzer) measuring the same blood samples. This is not explicitly stated but is the standard practice for such devices.

8. The sample size for the training set:

   • Not explicitly provided in the text. This document describes a 510(k) submission, which focuses on performance data for substantial equivalence, not the development or training of an algorithm in the machine learning sense. While the device utilizes embedded firmware and hardware, the concept of a "training set" in the context of AI or machine learning is not discussed for this type of device.

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

   • Not applicable/provided in the text. As mentioned above, a "training set" in the context of algorithm development for machine learning is not discussed for this device.

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