Search Results

The CLEVER CHEK Auto-Code Voice Blood Glucose Monitoring System is intended for use in the quantitative measurement of glucose in fresh capillary whole blood 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, and is not intended for use on neonates.

The alternative site testing in the CLEVER CHEK Auto-Code Voice Blood Glucose Monitoring System can be used only during steady-state blood glucose conditions.

This system contains a speaking functionality which provides step by step instructions to aid visually impaired persons.

CLEVER CHEK Auto-Code Voice Blood Glucose Monitoring System

This looks like a 510(k) clearance letter for a blood glucose monitoring system, not a study report. Therefore, much of the requested information about acceptance criteria and study details for an AI/ML device is not directly available in these documents.

However, I can extract the general indication for use which implies the functional requirements of the device and how it is expected to perform, and then discuss what kind of studies would typically be done for such a device to meet acceptance criteria, even if the specifics aren't here.

Based on the provided document, the following information can be extracted or reasonably inferred:

The document describes the CLEVER CHEK Auto-Code Voice Blood Glucose Monitoring System, intended for quantitative measurement of glucose in fresh capillary whole blood.

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

The provided document does not explicitly list acceptance criteria or reported device performance in a table format. For blood glucose monitoring systems, performance is typically assessed against accuracy standards. A common standard is ISO 15197 for in vitro diagnostic test systems, which typically requires a certain percentage of results to fall within specific accuracy ranges when compared to a laboratory reference method.

• Inferred Performance Requirement (based on device type): The device is intended for "quantitative measurement of glucose." This implies accuracy and precision.
• Typical Acceptance Criteria (based on similar devices, not from this document):
  • For glucose concentrations < 75 mg/dL (4.2 mmol/L), at least 95% of results must be within ±15 mg/dL (±0.83 mmol/L) of the reference measurement.
  • For glucose concentrations ≥ 75 mg/dL (4.2 mmol/L), at least 95% of results must be within ±20% of the reference measurement.

The document does not provide the reported device performance against such criteria. This information would typically be in the 510(k) summary or the full submission, not the clearance letter itself.

Regarding the speaking functionality: The document states, "This system contains a speaking functionality which provides step by step instructions to aid visually impaired persons." The acceptance criteria for this would likely involve user studies demonstrating the clarity, accuracy, and usability of the voice prompts for the target user group. Again, no specific study details are in this document.

2. Sample size used for the test set and the data provenance

The document does not provide details on the sample size or data provenance for any test set. For a blood glucose meter, the test set would typically involve human subjects whose blood glucose levels are measured by both the device under test and a laboratory reference method.

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

This information is not available in the provided document. For blood glucose meters, the "ground truth" is typically established by a highly accurate laboratory reference method (e.g., YSI analyzer) operated by trained laboratory personnel, rather than interpretation by clinical experts.

4. Adjudication method for the test set

This information is not available in the provided document. As noted above, for blood glucose meters, ground truth is usually established by a single, high-precision laboratory reference method, so traditional adjudication methods among clinical experts are not typically relevant here.

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

Not applicable. This device is a blood glucose meter, not an AI/ML-based diagnostic imaging system or similar. There are no "human readers" interpreting medical images or complex data in the context of this device. Its function is to quantitatively measure glucose.

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

This device is inherently a standalone device in its primary function – it measures blood glucose and displays the result. The "algorithm" is the biochemical reaction on the test strip and the meter's internal calculation to convert electrical signal to glucose concentration. It provides a numerical output without requiring human interpretation of raw data. The speaking functionality is an enhancement but doesn't change the standalone nature of the primary measurement.

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

For blood glucose meters, the ground truth is typically established using a laboratory reference method known for its high accuracy and precision, such as a YSI glucose analyzer.

8. The sample size for the training set

The document does not provide information on the sample size for a training set. While the internal algorithms of a blood glucose meter are developed and calibrated, the concept of a "training set" as understood in AI/ML is not directly applicable in the same way. Calibration data would be used, but specific "training set" details are not provided.

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

Similar to point 8, the document does not provide details on how "ground truth" was established for a training set. For the calibration of a blood glucose meter, the device is typically tested against various known concentration levels of glucose solutions and compared to results from a laboratory reference method to establish its calibration curve and ensure accurate readings across the physiological range.

Ask a specific question about this device