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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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