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The CLEVER CHOICE Auto-Code Pro Blood Glucose Monitoring System is intended for use in the quantitative measurement of glucose in fresh capillary whole blood samples from the finger and the following alternative sites: the palm, the forearm, the upper-arm, the calf and the thigh. It is intended for use by healthcare professionals and people with diabetes mellitus at home as an aid in monitoring the effectiveness of diabetes control program. It is not intended for the diagnosis of or screening for diabetes mellitus.

The CLEVER CHOICE Auto-Code Pro Blood Glucose Monitoring System cannot be used on neonates.

The alternative site testing in above systems can be used only during steady-state blood glucose conditions.

The CLEVER CHOICE Auto-Code Pro Blood Glucose Monitoring System is designed to quantitatively measure the concentration of glucose in fresh capillary whole blood. The test principle of system utilizes an electrochemical method-based meter and dry reagent biosensor (test strips) for blood glucose testing. The size of the current is proportional to the amount of glucose present in the sample, providing a quantitative measurement of glucose in whole blood and control solutions.

This 510(k) summary provides very limited information regarding the performance studies of the CLEVER CHOICE Auto-Code Pro Blood Glucose Monitoring System. It states that the device has the "same performance characteristics as the predicate device" and that "studies of software verification and validation testing, method comparison, and meter reliability test demonstrated that the performance of systems meets the intended use." However, it does not provide any specific acceptance criteria or detailed results of these studies.

Therefore, for almost all of your requested information, the document explicitly states that the device has the 'same performance characteristics as' the predicate, but does not identify these characteristics, or describe tests that establish them. As a result, I must indicate that most of the criteria cannot be met based on the provided text.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

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

• Sample size for the test set: Not provided.
• Data provenance: Not provided (e.g., country of origin, retrospective/prospective).

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 as no information on ground truth establishment by experts is provided for a test set.

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

• Not applicable as no details of a test set with expert adjudication are provided.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned. The device is a Blood Glucose Monitoring System, not an AI-assisted diagnostic tool for human readers.

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

• The document implies standalone performance of the blood glucose monitor through "method comparison, and meter reliability test," but it does not explicitly detail these studies or provide specific data regarding standalone performance. Without specific details on the "method comparison" test, it's impossible to confirm the algorithmic performance in isolation.

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

• Not explicitly stated for the "method comparison" studies. For blood glucose monitoring systems, ground truth is typically established by comparing results from the device to a laboratory reference method (e.g., a YSI analyzer), rather than expert consensus, pathology, or outcomes data. However, the document does not specify the ground truth method.

8. The sample size for the training set

• Not provided. The document does not mention a "training set" in the context of machine learning, as this is a traditional medical device, not an AI/ML product. The studies mentioned ("software verification and validation testing, method comparison, and meter reliability test") are standard performance evaluations for such devices.

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

• Not applicable, as no training set (in the ML sense) is mentioned or implied.

Summary of the Study Information Provided in the Document:

The 510(k) submission for the CLEVER CHOICE Auto-Code Pro Blood Glucose Monitoring System relies heavily on its substantial equivalence to a predicate device (FORA G30 Blood Glucose Monitoring System, K090187). It states that the proposed device has the "same performance characteristics" as the predicate and that the "intended use, test principle, and operating technology" are the same. The only difference is an "engineering modification."

The performance studies mentioned are:

• Software verification and validation testing
• Method comparison
• Meter reliability test

The document concludes that these studies "demonstrated that the performance of systems meets the intended use." However, no specific details, data, acceptance criteria, or methodologies for these studies are provided within the given text. This level of detail is typically found in the full 510(k) submission and supporting documentation, which is not included here.

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