(146 days)
The GlucoSure Voice Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Testing is done outside the body (In Vitro diagnostic use). The meter includes voice functionality to assist visually impaired users. It is indicated for both lay use by people with diabetes and in a clinical setting by healthcare professionals, as an aid to monitoring levels in Diabetes Mellitus.
The purpose of the control solution test is to validate the performance of the Blood Glucose Monitoring system using a testing solution with a known range of glucose. A control test that falls within the acceptable range indicates the user's technique is appropriate and the test strip and meter are functioning properly.
GlucoSure Voice consists of a meter, test strips, and control solutions for use in measuring blood glucose as an aid to monitor the effectiveness of diabetes control.
The provided 510(k) summary for the GlucoSure Voice Blood Glucose Monitoring System offers limited details regarding specific acceptance criteria and the comprehensive study that proves the device meets them. Blood glucose monitoring systems typically demonstrate performance against ISO 15197 standards (or similar regulatory guidance at the time of submission), which specify accuracy requirements. This document, however, mainly provides a high-level overview of the testing conducted.
Here's an attempt to extract and synthesize the information based on the provided text, while acknowledging the limitations:
1. Table of Acceptance Criteria and Reported Device Performance
The document states that "Results met pass/fail performance criteria," but it does not explicitly list these criteria with quantitative targets. For blood glucose meters, typical acceptance criteria involve accuracy thresholds, often expressed as percentages of readings within a certain range of a reference method, or within a specified error grid. Without this specific detail in the provided text, a table can only approximate based on standard expectations for such devices.
| Metric (Implied) | Acceptance Criteria (Implied for this type of device) | Reported Device Performance |
|---|---|---|
| Accuracy (Overall for Fingerstick & AST) | Typically, a high percentage of readings within ±15% or ±20% of a reference method for various glucose concentrations (e.g., in accordance with ISO 15197 or similar guidance). Performance within a specific zone on a Clarke Error Grid. | "Results met pass/fail performance criteria." (Specific numerical data not provided in the summary.) |
| Precision | Defined coefficient of variation (CV) or standard deviation at various glucose levels. | "Non-clinical testing of precision... was also conducted." (Specific numerical data not provided in the summary.) |
| Linearity | Performance over the device's stated measuring range. | "Non-clinical testing of ...linearity... was also conducted." (Specific numerical data not provided in the summary.) |
| Environmental Effects (Temperature, Humidity) | Stable performance within specified environmental conditions. | "Non-clinical testing of ...temperature and humidity effects was also conducted." (Specific numerical data not provided in the summary.) |
| Voice Functionality | Usability and effectiveness for visually impaired users. | "The clinical evaluation included testing of the voice functionality with visually impaired persons with diabetes." (Specific numerical data not provided in the summary.) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: The document does not specify the exact number of participants or samples in the clinical testing. It mentions "Clinical testing was done with persons with diabetes."
- Data Provenance: The country of origin of the data is not explicitly stated. The submitter is based in China (Taiwan), but this does not necessarily mean the clinical study was conducted there. The study was prospective, as it involved "persons with diabetes to verify proper performance."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not provide details on the number of experts, their qualifications, or their role in establishing the ground truth. For blood glucose meters, the "ground truth" (reference method) is typically established by laboratory-grade glucose analyzers, not by human expert consensus on interpretations.
4. Adjudication Method for the Test Set
Not applicable in the conventional sense for blood glucose meter accuracy studies. The comparison is generally against a quantitative reference method, not subjective interpretations requiring adjudication.
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 monitoring system, not an imaging or diagnostic device that involves human "readers" interpreting cases with or without AI assistance. The voice functionality assists visually impaired users in operating the device, but it's not an AI assisting human interpretation of medical data in an MRMC context.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, in a way. The performance of the GlucoSure Voice meter itself (the algorithm and hardware for glucose measurement) without human interpretation is the primary focus of the accuracy clinical testing. The meter generates a numerical glucose reading, and its accuracy is assessed against a reference method. The human "in-the-loop" aspects relate to proper sample collection and device operation, not interpretation of an algorithm's output in the diagnostic sense.
7. The Type of Ground Truth Used
- The ground truth for the accuracy testing was presumably derived from a laboratory reference method (e.g., a YSI glucose analyzer or a similar high-accuracy clinical chemistry analyzer) for glucose measurement. The summary states: "Professional fingertip meter results were compared with AST results collected both by professionals and by persons with diabetes." This implies a comparison to a more accurate, professionally obtained reading or a lab reference.
8. The Sample Size for the Training Set
- The document does not mention a "training set" in the context of machine learning or AI models. Blood glucose meters typically do not involve such training sets in the same way modern AI algorithms do. Their performance is based on the underlying electrochemical or enzymatic reaction and calibration, not a trained statistical model in the AI sense.
9. How the Ground Truth for the Training Set Was Established
- Not applicable, as no training set (in the AI/ML sense) is described or implied for this type of device.
§ 862.1345 Glucose test system.
(a)
Identification. A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.(b)
Classification. Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.