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The XPER Technology PREMIUM Pro Blood Glucose Monitoring System is intended for point-of-care, in vitro diagnostic, multiple-patient use for the quantitative determination of glucose in fresh capillary whole blood samples from the fingertips in endocrinology clinic laboratories and physician office laboratories.

The system should only be used with single-use, auto-disabling lancing devices when performing a fresh capillary whole blood sample from the fingertip.

The system is not intended for the screening or diagnosis of diabetes mellitus but is indicated for use in determining dysglycemia.

The system is not intended for use on patients receiving intensive medical intervention/therapy.

The system is not intended for use in acute care, nursing facilities, skilled nursing facilities or hospital settings. The system is not intended for use on neonates.

The XPER Technology PREMIUM Pro Blood Glucose Monitoring System consists of the XPER Technology PREMIUM Pro Blood Glucose Meter, the XPER Technology PREMIUM Pro Blood Glucose Test Strips, and the TaiDoc Blood Glucose Control Solutions. This system is a multiplepatient use for the quantitative determination of glucose in capillary whole blood samples from the fingertips in endocrinology clinic laboratories and physician office laboratories as an aid in monitoring the effectiveness of glucose control. The TaiDoc Blood Glucose Control Solutions are used to check that the meter and test strips are working together properly.

Here's a breakdown of the acceptance criteria and study details for the XPER Technology PREMIUM Pro Blood Glucose Monitoring System, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria are implicitly defined by the statement "all test results were within acceptance criteria" in various sections. The reported performance is the achievement of these criteria. The specific numerical targets for each criterion are not always explicitly stated (e.g., for precision, linearity, hematocrit, interference), but the document confirms that the device met these criteria.

Table of Acceptance Criteria and Reported Device Performance

Study Details

This document describes a medical device, a Blood Glucose Monitoring System, which does not utilize AI or involve human readers for image interpretation. Therefore, questions related to AI models, human reader improvement with AI assistance, expert adjudication for ground truth related to image analysis, or MRMC studies are not applicable to this device.

Here's the relevant information based on the provided text:

1. Sample sizes used for the test set and data provenance:

   • Clinical Accuracy (Capillary Blood): 414 patients.
     • Provenance: Clinical study conducted at 3 U.S. sites and 6 Taiwan sites. Data is prospective as it was collected during an active clinical study with patients.
   • Accuracy at Extremes: 100 samples (50 for 300 mg/dL).
     • Provenance: "Blood samples were collected and allowed to glycolyze or were spiked with high concentration glucose solution". Implies a laboratory-controlled, prospective or specially prepared sample set.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable in the traditional sense of 'experts' interpreting data. For blood glucose monitoring systems, the "ground truth" is established by a highly accurate reference method.
   • The ground truth for the clinical and extreme glucose value studies was the YSI Model 2300 Glucose Analyzer. This is a laboratory-based, well-established, and highly accurate reference method for glucose measurement, not human experts.

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

   • Not Applicable. As ground truth is established by an automated reference analyzer (YSI-2300), there is no human interpretation or adjudication process involved in setting the ground truth for glucose values.

4. 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:

   • Not Applicable. This is a blood glucose monitoring system, not an AI-assisted diagnostic imaging device. There are no human readers or AI assistance in the interpretation of results from this device in the same way as an imaging study.

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

   • Yes, in essence. Blood glucose meters are inherently "standalone" in their function of measuring glucose. The clinical study directly compares the device's numerical output (algorithm's result) to the YSI-2300 reference method without human interpretation of the device's output influencing the direct comparison. The "human-in-the-loop" for this device is the user taking and reading the measurement, but the performance evaluation is on the accuracy of the numerical reading itself, independent of user interpretation for the primary outcome.

6. The type of ground truth used:

   • Reference Method: The YSI Model 2300 Glucose Analyzer, a laboratory-based, highly accurate method for quantitative glucose determination. Comparisons are quantitative.

7. The sample size for the training set:

   • Not directly applicable/not explicitly stated in terms of an "AI training set." This device is a traditional electrochemical biosensor, not an AI/machine learning model that undergoes a distinct "training" phase with a large dataset in the way a deep learning algorithm would. The development and calibration of such a device involve extensive laboratory testing and optimization, which could be considered an analogous "training" or development process for its internal algorithms, but it's not described as a separate, quantifiable "training set" with ground truth in the context of AI regulatory submissions.

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

   • Not applicable in the AI context. For this type of device, ground truth for development/calibration (analogous to "training") would be established through a combination of:
     • Highly controlled laboratory experiments using reference solutions of known glucose concentrations.
     • Comparisons to established reference methods (like YSI-2300) with well-characterized samples.
     • Controlled studies to characterize and mitigate interferences (e.g., hematocrit, medications).

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