Search Results

The Clever Chek TD-4225 Blood Glucose Test System is intended for use in the quantitative measurement of glucose in whole blood taken from the finger. 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.

Not Found

This document (K051854) is a 510(k) premarket notification for a glucose test system, which does not typically include the detailed statistical study design and results that would be found in a clinical trial report for an AI/ML medical device. Therefore, much of the requested information cannot be extracted directly from this document.

However, I can provide the available information based on the typical requirements for glucose meters and what is implied by such a submission.

Here's the information that can be gleaned or reasonably inferred:

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

For glucose meters, the acceptance criteria are generally based on ISO 15197 (or similar standards at the time of submission). These standards typically involve accuracy parameters like:

• System Accuracy: Percentage of results within specific error margins when compared to a laboratory reference method. For example, within ±15 mg/dL or ±15% of the reference value.
• Precision (Repeatability and Reproducibility): How close repeated measurements are to each other.

Since this is a 510(k) clearance document, the specific numerical acceptance criteria and a detailed table of reported device performance values (e.g., bias, CV, % within error limits) are not provided in this summary letter. These detailed results would be in the full submission, which is not publicly available as part of this clearance letter.

However, the device's clearance indicates that it met the FDA's requirements for substantial equivalence, which includes meeting performance standards that would have been defined in the submission for accuracy and precision.

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

• Sample Size for Test Set: Not explicitly stated in the provided document. For glucose meters, study protocols typically involve testing a significant number of samples across the analytical range, often involving hundreds of measurements from multiple individuals.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not explicitly stated. Clinical studies for medical devices are usually prospective, meaning data is collected specifically for the study. Given the manufacturer is based in Taiwan, it's highly probable that the data
  would have been collected in Taiwan or potentially across multiple sites as part of a multicenter study.

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

• Not applicable in the context of a glucose meter. The "ground truth" for a glucose meter is established by a highly accurate laboratory reference method (e.g., a YSI glucose analyzer or a hexokinase method). It does not rely on expert consensus or interpretation in the way an imaging device might. The "experts" involved would be trained laboratory technicians operating the reference instruments.

4. Adjudication method for the test set

• Not applicable. The "ground truth" is determined by direct laboratory measurement, not by human adjudication of interpreted results.

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 standalone diagnostic tool (glucose meter), not an AI-powered assistive device for human readers. Therefore, an MRMC study and the concept of "human readers improving with AI" does not apply.

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

• Yes, this is an inherent characteristic of the device. A glucose meter like the Clever Chek TD-4225 operates in a standalone manner. The user applies a blood sample, and the device provides a numerical result. Its performance is evaluated entirely on its ability to accurately measure glucose concentrations independently.

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

• The ground truth for a glucose test system is established by a highly accurate laboratory reference method, such as:
  • YSI Glucose Analyzer: Widely considered a gold standard for glucose measurement in clinical studies.
  • Hexokinase method: Another highly accurate enzymatic method for glucose determination, often used in central laboratories.

8. The sample size for the training set

• Not applicable in the common sense of "training set" for AI/ML devices. This device is a chemical/eletrochemical system. While its internal algorithms or calibrations are developed using data, it's not "trained" in the machine learning sense on a distinct training set of patient data to learn patterns. The calibration and robust performance are developed through extensive electrochemical and engineering studies.

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

• Not applicable for a "training set" in the AI/ML context. The initial calibration and validation of the device's internal algorithms (e.g., how the electrical signal is converted to a glucose reading) would be done against a well-controlled set of glucose solutions of known concentrations, confirmed by a laboratory reference method. This is part of the engineering and manufacturing process, not a traditional "training set" like for AI.

Ask a specific question about this device