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The MDT2 BLE Self Monitoring Blood Glucose System is intended for the quantitative measurement of glucose in fresh capillary whole blood from fingertip, palm, or forearm. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by a single patient with diabetes and should not be shared, as an aid to monitor the effectiveness of diabetes control. The system is not to be used on neonates, nor for the diagnosis of, or screening for diabetes mellitus. Alternative site testing can be only used during steady-state blood glucose conditions.

The system consists of the MDT2 BLE meter and the MDT2 BLE test strips. The MDT2 BLE meter only is used with the MDT2 BLE test strips to quantitatively measure glucose in fresh capillary whole blood from fingertip, palm, or forearm.

The modified device of MDT2 BLE glucose meter is derived from the existing device of EM40 glucose meter and the modified device contain the Bluetooth function to transfer glucose results to the mobile app.

The self-monitoring blood glucose system consists of a blood glucose meter and test strips which are designed, tested, and verified to work together as a system to produce accurate blood glucose test results.

The electrochemical principle on the test strip is the reaction of FAD glucose dehydrogenase (FAD-GDH) with blood glucose and a small electrical current generated proportional to the glucose concentration in the blood sample. The meter measures the current and displays the blood glucose result.

The provided text does not contain detailed information about a study proving the device meets acceptance criteria, specifically not in the format requested for a scientific study with sample sizes, expert qualifications, or comparative effectiveness.

However, based on the information provided, here's what can be inferred and presented, focusing on the acceptance criteria as stated in the context of substantial equivalence for a medical device (MDT2 BLE Self-Monitoring Blood Glucose System):

The document indicates that the MDT2 BLE Self-Monitoring Blood Glucose System is considered "substantially equivalent" to its predicate device, the EM40 Self-Monitoring Blood Glucose System (K133389). This implies that the acceptance criteria for the MDT2 BLE system are aligned with those previously met by the EM40 system, particularly since most core functionalities and technologies remain the same.

The modifications to the MDT2 BLE system are primarily related to its physical design, software features (memory mode, measurement mode, reset function, display icons), and the addition of Bluetooth functionality for wireless data transfer. These modifications were subjected to risk analysis and verified/validated.

Given the information, I can construct a table for what could be considered "acceptance criteria" based on the predicate device's performance, and then note that the new device states it meets these as part of its "substantial equivalence" claim.

Acceptance Criteria for MDT2 BLE Self-Monitoring Blood Glucose System and Reported Device Performance

Regarding the specific questions you asked:

1. A table of acceptance criteria and the reported device performance: See the table above. The "acceptance criteria" are implied by the predicate device's specifications, and the "reported device performance" is the claim of "same as predicate" or confirmed equivalence through V&V activities.

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

   • The document mentions "verification and validation activities" and a "software validation report" but does not specify sample sizes for any test sets used in these activities.
   • Data provenance is not explicitly stated in terms of country of origin or whether studies were retrospective or prospective, beyond the fact that the company is located in Taiwan.

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):

   • This information is not provided in the document. The document refers to "risk analysis" and "verification and validation activities" but not to specific expert panels or their qualifications for establishing ground truth derived from clinical studies. Given this is a self-monitoring blood glucose system, ground truth would typically be established against laboratory reference methods.

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

   • This information is not 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:

   • This is not applicable to this device. An MRMC study is typically for evaluating diagnostic imaging systems where human readers interpret medical images. This device is a blood glucose monitoring system. Its performance is compared to laboratory reference methods, not human interpretation of images.

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

   • The device is an in-vitro diagnostic device that measures glucose. The "algorithm" here refers to the meter's internal processing of an electrochemical signal into a glucose reading. Its performance is indeed "standalone" in the sense that the meter itself provides the quantitative measurement without human interpretation of the raw signal. The document states "The meter measures the current and displays the blood glucose result." The standalone performance is implicitly claimed through the "substantial equivalence" to the predicate and the verification/validation activities of the new meter's components, including software/firmware changes.

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

   • For blood glucose meters, the ground truth is typically established against laboratory reference methods for glucose measurement (e.g., YSI analyzer). While not explicitly stated here, this is the standard practice for such devices. The document implies verification and validation against established standards and the predicate device, which would have been validated against such ground truth.

8. The sample size for the training set:

   • This information is not provided. The document doesn't detail specific training sets, which is typical for traditional (non-AI/ML) medical devices like blood glucose meters. Even for the software/firmware changes, the term "training set" is not used in this context.

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

   • As no training set is mentioned in the context of AI/ML, this is not applicable. For the device's inherent function, ground truth for calibration and testing is against laboratory reference methods.

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