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MIO Blood Glucose Monitoring System is comprised of the MIO Blood Glucose Meter and the MIO Blood Glucose Test Strips. MIO Blood Glucose Monitoring System is intended to quantitatively measure the glucose concentration in fresh capillary whole blood samples drawn from the fingertips. It is intended for use by persons with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for neonatal use or for the diagnosis of or screening for diabetes. This system is intended for self-testing outside the body (in vitro diagnostic use), and should only be used by a single person and should not be shared.

MIO Blood Glucose Monitoring System is designed to quantitatively measure the glucose concentration in fresh capillary whole blood. The glucose measurement is achieved by using the amperometric detection method. The test is based on measurement of electrical current caused by the reaction of the qlucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with glucose oxidase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration in the sample is displayed. MIO Blood Glucose Monitoring System contains 4G module, the device complies with US federal guidelines, FCC Part 15 Subpart B, FCC Part 24 Subpart E, FCC Part 27 Subpart C, and FCC 47 CFR§ 2.1093 based on the test reports.

The MIO Blood Glucose Monitoring System is a glucose test system designed for quantitative measurement of glucose concentration in fresh capillary whole blood samples. The acceptance criteria and performance of the device are derived from the information provided in the Sections E: 510(k) Summary and Indications for Use.

It's important to note that the provided text primarily focuses on the device's substantial equivalence to a predicate device and lists various laboratory and clinical studies conducted. However, it does not explicitly detail the specific acceptance criteria values or the reported performance outcomes for most of these studies. It broadly states that "the test results indicated that the acceptance criteria were met" and "the performances from these laboratory studies were acceptable." The only quantitative performance data provided is related to the comparison with the predicate device (e.g., measurement range, sample volume, test time).

Given the limitations of the provided text, I will construct the table and responses based on the available information regarding the device's characteristics and the general statements about meeting acceptance criteria. For elements not explicitly quantified, I will indicate that the information is "Not explicitly stated/quantified in the provided document."

Acceptance Criteria and Device Performance

The provided document indicates that the MIO Blood Glucose Monitoring System underwent various laboratory and user evaluation studies, and the results met the acceptance criteria. While specific numerical acceptance criteria and corresponding performance values for all tests are not detailed, the document emphasizes substantial equivalence to a predicate device, which implies adherence to recognized standards for glucose monitoring systems.

Table of Acceptance Criteria and Reported Device Performance

*Note: Specific numerical acceptance criteria were not provided in the document. The descriptions reflect general expectations for such devices based on regulatory guidance (e.g., FDA guidance for SMBG for OTC Use, and ISO 15197:2013 for glucose monitoring systems, which the report states the device meets).

Study Details:

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

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 Test Set: The document lists "User Evaluation" and "User Evaluation - Accuracy at Extreme Glucose Values" as key studies, implying test sets of human subjects. However, the exact sample sizes for these studies are not explicitly stated in the provided text. For laboratory studies (e.g., precision, linearity, interference), the sample sizes (e.g., number of replicates, number of interfering substances) are also not explicitly stated.
   • Data Provenance: The document does not specify the country of origin for the data. The studies appear to be prospective as they are described as "performed" or "conducted" to evaluate the device.

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)
   The document describes a "User Evaluation" where "non-professional, inexperienced lay persons" were able to obtain blood glucose readings. The ground truth for blood glucose monitoring systems is typically established using a highly accurate laboratory reference method (e.g., YSI analyzer) performed by trained laboratory personnel. The document does not explicitly state the number of experts or their specific qualifications for establishing the ground truth, but it implies standard laboratory practices were followed as "laboratory studies were performed... in accordance with the applicable guidance or standards." No specific "expert" human readers are mentioned in the context of interpreting results or establishing ground truth beyond the users themselves taking their readings.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
   The concept of "adjudication" (e.g., multiple readers resolving discrepancies) is typically relevant for interpretative diagnostic devices (e.g., radiology AI). For a blood glucose monitoring system, the ground truth is established by a quantitative laboratory reference method, not by expert interpretation requiring adjudication. Therefore, an adjudication method in this sense is not applicable and was not performed.

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
   An MRMC study is relevant for AI-assisted diagnostic tools where human interpretation is part of the workflow. The MIO Blood Glucose Monitoring System is a standalone measurement device for self-testing (OTC Use). There is no AI component mentioned in the device's description that assists human readers in interpreting results. Therefore, an MRMC comparative effectiveness study was not done, and effect sizes related to human improvement with AI assistance are not applicable.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
   The MIO Blood Glucose Monitoring System is a device that provides a direct quantitative measurement of blood glucose. Its core function is an "algorithm only" in the sense that the device's internal processing yields the glucose concentration. The "User Evaluation" study assesses the device's performance when used by lay persons, which inherently involves human interaction with the device (applying blood, reading results). However, the accuracy metrics themselves (e.g., precision, linearity, interference) tested in "Laboratory Testing" are inherent to the device's standalone performance, independent of human interpretive input. So, yes, its standalone performance was evaluated extensively across multiple laboratory studies (e.g., linearity, precision, interference, environmental stability).

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
   The ground truth for a Blood Glucose Monitoring System is established by comparison to a highly accurate laboratory reference method. This is standard practice for evaluating glucose meters. The document does not explicitly name the reference method used (e.g., YSI glucose analyzer), but it's implied by the nature of the studies (e.g., "Accuracy at Extreme Glucose Values" would require a precise reference).

8. The sample size for the training set
   The document describes the device as a glucose monitoring system, not an AI/ML device that requires a "training set" in the context of machine learning model development. The system's operation is based on an electrochemical biosensor and fixed algorithms for calculating glucose concentration from electrical current. Therefore, a "training set" in the common AI/ML sense is not applicable for this type of device.

9. How the ground truth for the training set was established
   As there is no "training set" in the AI/ML context for this device, the question of how its ground truth was established is not applicable. The ground truth for calibrating the device during manufacturing and for validating its performance is based on highly accurate laboratory reference methods as described in point 7.

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