Search Results

The WaveSense Jazz Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood from the finger stick, palm and/or forearm. Testing is done outside of the body (in vitro diagnostic use). It is indicated for use at home (over the counter (OTC)) by persons with diabetes, as an aid to monitor the effectiveness of diabetes control.

The WaveSense Jazz Blood Glucose Monitoring System includes a meter with batteries, compact carrying case, lancing device lancets, control solution and instructions for use. Test Strips are necessary for testing but are sold separately.

The WaveSense Jazz Blood Glucose Monitoring System is intended for the quantitative measurement of blood glucose levels in fresh capillary whole blood samples drawn from the fingertips, palm or forearm. The WaveSense Jazz Test Strips are for in vitro diagnostic (outside of the body) use only. The WaveSense Jazz System is not intended for use with neonates.

The provided text describes a 510(k) premarket notification for the WaveSense Jazz Blood Glucose Monitoring System. The document focuses on establishing substantial equivalence to a predicate device (K072413), rather than presenting a de novo clinical study with detailed acceptance criteria and performance data as typically seen for novel devices, especially those incorporating AI.

Therefore, the information required to fully answer the request regarding acceptance criteria and a study proving device performance (especially for an AI/ML context) is largely absent from this specific 510(k) summary. The document primarily discusses the intended use, technological comparison to a predicate, and the modifications made (new colors, new data management feature), implying that much of the performance data would have been established for the original predicate device.

However, I can extract the relevant information that is present and indicate where information is not available from the provided text.

Here's an attempt to answer based on the provided document, acknowledging its limitations for an AI/ML-centric request:

Acceptance Criteria and Device Performance (based on the provided 510(k) Summary for a Glucose Monitoring System)

It's crucial to understand that this 510(k) pertains to a Blood Glucose Monitoring System, which is a hardware-based diagnostic device, not an AI/ML-powered software or imaging device. Therefore, many of the typical questions regarding AI/ML clinical studies (MRMC, expert consensus for ground truth, training set details, etc.) are not applicable to this type of submission.

The "study" referenced in the provided text is primarily focused on verification and validation (V&V) of the modifications made to an existing predicate device, rather than a large-scale clinical trial to establish novel performance.

1. Table of Acceptance Criteria and Reported Device Performance

For a Blood Glucose Monitoring System, acceptance criteria usually relate to accuracy standards (e.g., ISO 15197 for point-of-care testing), precision, and other analytical performance characteristics. The provided 510(k) summary does not explicitly list these numerical acceptance criteria or the specific performance results in a table. It instead states that "verification and validation results" were sufficient to establish substantial equivalence.

However, based on typical FDA requirements for Blood Glucose Monitoring Systems, the implicit acceptance criteria would relate to:

2. Sample Size Used for the Test Set and Data Provenance

The document mentions "verification and validation results" and "firmware functional testing and usability engineering evaluations" but does not specify the sample size for any test sets.

• Data Provenance: Not explicitly stated, but typically for such devices, the data would be collected from human subjects (e.g., finger-stick blood samples). The document does not specify country of origin or whether it was retrospective or prospective. Given the nature of a 510(k) for a modified device, it's likely a combination of bench testing (prospective), and potentially limited human use testing (prospective) to validate the specific changes.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts

This is not applicable in the context of a Blood Glucose Monitoring System where the "ground truth" for glucose concentration is established by a laboratory reference method (e.g., hexokinase method on a central laboratory analyzer), not by human expert interpretation (like a radiologist for imaging).

4. Adjudication Method for the Test Set

This is not applicable as there is no human interpretation or subjective assessment that would require adjudication for a glucose reading.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size of Human Improvement with AI vs. Human without AI Assistance

This is not applicable. This is a hardware-based diagnostic device for measuring glucose, not an AI-powered system designed to assist human readers in, for instance, interpreting images or making clinical diagnoses.

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

This is not applicable. The device is the "algorithm" and measurement system. Its performance is measured directly against a reference method. It's not an AI algorithm that produces an output that then needs to be compared to human performance.

7. The Type of Ground Truth Used

For a Blood Glucose Monitoring System, the primary ground truth for glucose concentration is laboratory reference methods (e.g., YSI 2300 STAT Plus Glucose & Lactate Analyzer, or similar enzymatic methods traceable to national/international standards), typically using venous blood plasma samples.

8. The Sample Size for the Training Set

This concept of a "training set" is primarily relevant for machine learning/AI models. For a traditional electrochemical glucose sensor, there isn't a "training set" in the same sense. The device is calibrated during manufacturing based on known glucose concentrations, and its accuracy is verified and validated. No information on a "training set" is provided or applicable here.

9. How the Ground Truth for the Training Set was Established

As above, the concept of a "training set" and its associated ground truth is not applicable for this type of device.

Ask a specific question about this device