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510(k) Data Aggregation
(233 days)
The iHealth® Align Gluco-Monitoring System (BG1) consists of the iHealth® Align Glucose meter (BG1), iHealth® Blood Glucose Test Strips (EGS-2003), and the iHealth® Gluco-Smart App mobile application as the display component of the iHealth® Align Gluco-Monitoring System. The iHealth® Align Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, upper arm, calf, or thigh. The iHealth® Align Gluco-Monitoring System is intended to be used by a single person and should not be shared.
The iHealth® Align Gluco-Monitoring System (BG1) is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth® Align Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady - state times (when glucose is not changing rapidly).
The iHealth® Wireless Smart Gluco-Monitoring System (BC5) consists of the iHealth® Wireless Glucose meter (BC5), iHealth® Blood Glucose Test Strips (EGS-2003), and the iHealth® Gluco-Smart App mobile application. The iHealth® Wireless Smart Gluco-Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertip, palm, forearm, calf, or thigh. The iHealth® Wireless Gluco-Monitoring System is intended to be used by a single person and should not be shared.
The iHealth® Wireless Smart Gluco-Monitoring System (BG5) is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The iHealth® Wireless Smart Gluco-Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. Alternative site testing should be done only during steady - state times (when glucose is not changing rapidly).
The iHealth® Align Gluco-Monitoring system(BG1) consist of BG1 glucose meter, EGS-2003 test strip, iHealth® control solution(Level I, Level II, Level III), lancet and lancing device. The BG1 glucose meter can be connected to iOS device and Android device through earphone jack, and display test result on iOS or Android device.
The iHealth® wireless Smart Gluco-Monitoring System(BG5) consist of BG5 glucose meter, EGS-2003 test strip, iHealth® control solution(Level I, Level II, Level III), lancet and lancing device. The BG5 glucose meter can display test result on meter itself, and can also be connected to iOS device and Android device through bluetooth and display test result on iOS or Android device.
This document describes the acceptance criteria and study proving the performance of the iHealth Align Gluco-Monitoring System (BG1) and the iHealth Wireless Smart Gluco-Monitoring System (BG5).
1. Table of Acceptance Criteria and Reported Device Performance
The provided document doesn't explicitly state quantitative acceptance criteria for glucose accuracy in a structured table. However, it references a "User Evaluation study" that confirmed "user accuracy." For typical glucose meters, acceptance criteria relate to the agreement between the device's reading and a laboratory reference method. Common standards, such as ISO 15197, require a certain percentage of results to fall within specific ranges of the reference value. Given the context of a 510(k) submission, it's implied that the device meets such performance standards.
Based on the information provided, the "reported device performance" is a general statement from a user evaluation study.
| Acceptance Criteria Category | Desired Performance | Reported Device Performance |
|---|---|---|
| User Accuracy | Confirmed as "user accurate" and "easy to use" | "The study results demonstrate that the user accuracy and ease of use (via participant questionnaire scoring) confirmed the proposed device to be substantially equivalent to the predicate device." |
| Precision | Not explicitly stated (implied to meet standard glucose meter precision) | Evaluated (Performance, functionality, and reliability evaluated) |
| Linearity | Not explicitly stated (implied to meet standard glucose meter linearity) | Evaluated (Performance, functionality, and reliability evaluated) |
| Interference | Not explicitly stated (implied to have acceptable interference profiles) | Evaluated (Performance, functionality, and reliability evaluated) |
| Sample volume | Minimum 0.7 microliter | Evaluated (Performance, functionality, and reliability evaluated); claimed same as predicate |
| Hematocrit Range | 20-60% | Evaluated (Performance, functionality, and reliability evaluated); claimed same as predicate |
| Operating Temperature Range | 10℃~35℃ (50°-95°F) | Evaluated (Performance, functionality, and reliability evaluated); claimed same as predicate |
| Measurement Range | 20mg/dL-600mg/dL (1.1mmol/L~33.3mmol/L) | Same as predicate |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: A total of 350 participants were included in the User Evaluation study.
- Data Provenance: The document does not specify the country of origin of the data. It also does not explicitly state whether the study was retrospective or prospective, but a "User Evaluation study" typically implies prospective clinical data collection.
3. Number of Experts and their Qualifications
The document does not specify the number of experts used to establish ground truth or clinical reference values. It also does not mention the qualifications of any experts. For glucose meters, ground truth is typically established using a highly accurate laboratory reference method, and not by human expert consensus in the same way it would be for image interpretation.
4. Adjudication Method
The document does not mention an adjudication method as it relates to human expert review. This is typical for glucose meter studies where ground truth is established by a quantitative laboratory reference method rather than subjective human interpretation.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not mentioned or conducted in this context. MRMC studies are primarily relevant for diagnostic imaging systems or other subjective interpretation tasks where human readers' performance is being evaluated and potentially improved by AI. This submission concerns a blood glucose monitoring system, a quantitative measurement device.
6. Standalone Performance Study
Yes, a standalone performance evaluation was conducted, although not in the typical "algorithm only without human-in-the-loop" sense for clinical AI software. The document states:
- "Performance, functionality, and reliability of the proposed device has been evaluated. The performance evaluation include precision, altitude, temperature & humidity, linearity, interference, sample volume and hematocrit."
This indicates rigorous testing of the device's technical specifications and accuracy independent of direct user interaction to establish its inherent measurement capabilities.
7. Type of Ground Truth Used
The document does not explicitly state the specific "type" of ground truth in terms such as "expert consensus" or "pathology." However, for blood glucose monitoring systems, the ground truth is established by a highly accurate laboratory reference method for blood glucose concentration. The performance characteristics like precision, linearity, and interference are all evaluated against these reference measurements.
8. Sample Size for the Training Set
The document does not mention or specify a training set sample size. This is because the device described is likely a traditional medical device (electro-chemical biosensor) and not an AI/ML-driven device that requires a distinct "training set" for model development. The "User Evaluation study" and "Performance, functionality, and reliability" evaluations would be comparable to a test set for a new device.
9. How Ground Truth for the Training Set Was Established
As there is no mention of a training set in the context of an AI/ML model, there is no information provided on how ground truth for a training set was established. The device relies on electrochemical biosensor technology, not machine learning that requires a training phase.
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