Search Results

The iHealth Wireless Smart Gluco-Monitoring System (iHealth Gluco+) consists of the iHealth Wireless Smart Glucose Meter (BG5S) and the iHealth Blood Glucose Test Strips (EGS-2003). The iHealth Wireless Smart Gluco-Monitoring System (iHealth Gluco+) 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 Wireless Smart Gluco-Monitoring System (iHealth Gluco+) is intended to be used by a single person and should not be shared.

The iHealth Wireless Smart Gluco-Monitoring System (iHealth Gluco+) 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 (iHealth Gluco+) 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 ( iHealth Gluco+ ) consist of BG5S glucose meter, EGS-2003 test strip, iHealth® control solution(Level I, Level III), lancet and lancing device. The BG5S 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.

The provided text is a 510(k) summary for the iHealth Wireless Smart Gluco-Monitoring System (iHealth Gluco+). This document describes the device, its intended use, and comparisons to a predicate device to demonstrate substantial equivalence.

However, it does not contain the detailed information required for a comprehensive answer to your questions regarding acceptance criteria and the study proving the device meets those criteria. Specifically, the document mentions:

• "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 that these tests were performed, but the acceptance criteria and the results of these tests (the actual reported device performance against those criteria) are not provided in this summary.
• "User Evaluation was completed for the iHealth Wireless Smart Gluco-Monitoring System(iHealth Gluco+)." This indicates a user study was done, but details about sample size, ground truth, expert involvement, or MRMC studies are missing.
• "The user accuracy and ease of use (via participant questionnaire scoring) confirmed the proposed device to be substantially equivalent to the predicate device." This states a conclusion but lacks the data to support it.

Therefore, I cannot populate the table or answer most of your detailed questions directly from the provided text. The document is a summary for regulatory clearance, not a full study report.

Here's what I can extract and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Test Set Sample Size: Not specified in the provided text.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document is a regulatory submission, so likely encompasses data generated specifically for the submission, but the details are absent.

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

• Not specified in the provided text. The "User Evaluation" implies human interaction but does not detail the establishment of ground truth by experts for the performance metrics. Ground truth for blood glucose is typically based on a laboratory reference method.

4. Adjudication method for the test set:

• Not specified. This would typically be relevant for studies involving human readers or interpretation (e.g., imaging studies), which isn't explicitly detailed here for a glucose meter.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, and its effect size:

• Not applicable / Not specified. MRMC studies are primarily for evaluating diagnostic imaging systems where multiple readers interpret cases. This device is a blood glucose meter. The "User Evaluation" mentioned might involve multiple users but is not an MRMC study in the traditional sense for diagnostic accuracy improvement.

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

• Implied for the meter's performance: The "Performance evaluation" (precision, linearity, etc.) implicitly assesses the device's standalone accuracy against a reference method. The results of these tests, however, are not provided. The phrase "user accuracy" refers to human operation of the device.

7. The type of ground truth used:

• For a blood glucose monitoring system, the ground truth for accuracy measurements (precision, linearity) would typically be established by a laboratory reference method (e.g., a YSI analyzer). This is not explicitly stated in the summary but is standard practice for these devices.
• For "user accuracy" and "ease of use," the ground truth would be based on the reference method for glucose levels and participant questionnaire scoring, respectively.

8. The sample size for the training set:

• Not applicable / Not specified. This refers to machine learning models. A blood glucose meter, operating on electrochemical biosensor principles, is not typically "trained" in the same way an AI/ML algorithm is. Its performance is determined by its physical and chemical design and calibration.

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

• Not applicable. See point 8.

In summary, the provided document is a 510(k) summary for a blood glucose meter, which focuses on demonstrating substantial equivalence to a predicate device. It lists the types of performance evaluations conducted but does not provide the quantitative acceptance criteria or the study results (device performance data) that would allow for a complete answer to your detailed questions. To get that level of detail, one would typically need to review the full 510(k) submission, not just the summary.

Ask a specific question about this device

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.

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.

Ask a specific question about this device

The iHealth Wireless Gluco-Monitoring System consists of the iHealth Wireless Glucose meter (BG5), iHealth Blood Glucose Test Strips (AGS-1000), and the iHealth Gluco-Smart App mobile application. The iHealth Wireless 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 Wireless Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth Wireless Gluco-Monitoring System 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 Cluco-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(BG5) consist of blood qlucose meter, single use test strips, sterile lancets, lancing device and the control solutions.

They are based on an electrochemical biosensor technology (electrochemical) and the principle of capillary action. Capillary action at the end of the test strip draws the blood into the action chamber and the blood glucose result is displayed in 5 seconds. The control solution available is used to test the performance of the device. It uses the same technological characteristics for testing with its predicate device.

In order to use theiHealth wireless Smart Gluco-Monitoring System(BG5), a compatible Android or iOS mobile device with the necessary mobile application installed is required.

The provided text describes a glucose monitoring system, not an AI device. As such, the typical acceptance criteria and study elements associated with AI are not directly applicable.

However, I can extract information related to the performance criteria and validation study for the iHealth Wireless Smart Gluco-Monitoring System (BG5) as presented in the 510(k) summary. This device is a blood glucose meter, and its performance is assessed against accuracy standards for glucose measurements.

Here's an interpretation based on the provided document:

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

The document doesn't explicitly state "acceptance criteria" in a table format with "reported device performance." However, it implies that the device meets the performance of its predicate device, as claimed in section 8.0. For glucose meters, the typical acceptance criteria are established by standards like ISO 15197 (Accuracy requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus). While these specific ISO criteria are not directly listed as "acceptance criteria" here, the declaration of "substantial equivalence" implies adherence to such recognized performance standards.

The document states:

• Measurement Range: 20mg/dL-600mg/dL (1.1mmol/L~33.3mmol/L)
• Test Time: 5 seconds
• Sample Volume: Minimum 0.7 microliter
• Hematocrit Range: 20-60%

The comparison table (Section 7.0) indicates that the new device has the same characteristics as the predicate device (K123935) for these performance parameters, implying it meets the same performance as the predicate.

The key statement regarding meeting criteria is in Section 8.0: "the test in this submission provides demonstration that these small differences [connect to Android] do not raise any new questions of safety and effectiveness." This implies that the device's accuracy and performance are considered equivalent to the predicate, which would have met its own set of performance criteria, likely aligning with ISO 15197 for glucose meters.

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

The document does not explicitly state the sample size used for performance testing (e.g., number of subjects, number of blood samples). It also doesn't specify the country of origin of the data or whether the study was retrospective or prospective. It only mentions "the test in this submission."

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

This information is not provided. For glucose meters, the "ground truth" is typically established by laboratory reference methods (e.g., YSI analyzer) rather than expert interpretation, so the concept of experts establishing ground truth in this context is less relevant.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

Not applicable, as this is for a glucose meter and not an AI or imaging device requiring human expert 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, as this is a standalone glucose meter, not an AI-assisted diagnostic tool.

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

Yes, the device itself is a standalone glucose monitoring system. Its performance is evaluated on its ability to accurately measure glucose levels independent of human interpretation or AI assistance. The output is a numerical glucose reading.

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

For a glucose meter, the ground truth is established by comparison to a laboratory reference method, typically a YSI glucose analyzer, which is considered highly accurate for quantitative glucose measurement in blood. This is standard for glucose meter validation, though not explicitly detailed in this summary.

8. The sample size for the training set:

Not applicable. This device is not an AI model requiring a "training set." It's a hardware medical device with electrochemical sensing technology.

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

Not applicable, as there is no training set for this type of device.

Ask a specific question about this device

The iHealth Align Gluco-Monitoring System consists of the iHealth Align Glucose meter (BG1), iHealth Blood Glucose Test Strips (AGS-1000), 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 Alian Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth Align Gluco-Monitoring System 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 BG5 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, upper arm, calf or thigh. The iHealth BG5 wireless Smart Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth BG5 wireless Smart Gluco-Monitoring System 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 BG5 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 AGS-1000I test strips are for use with the iHealth BG5 meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, palm, forearm, upper arm, calf or thigh.

The iHealth BG5L 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, upper arm, calf or thigh. The iHealth BG5L wireless Smart Gluco-Monitoring System is intended to be used by a single person and should not be shared.

The iHealth BG5L wireless Smart Gluco-Monitoring System 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 BG5L 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 AGS-1000I test strips are for use with the iHealth BG5L meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips, palm, forearm, upper arm, calf or thigh.

The iHealth Align Gluco-Monitoring System consists of a blood glucose meter, test strips, iHealth Gluco-Smart App, sterile lancets, lancing device and AGS-1000I Control Solutions (Level I. Level II and Level III). The iHealth Align Gluco-Monitoring System cannot display test results and must be used with an iPhone or iPod touch via an 3.5 mm auxiliary jack.

The iHealth BG5 wireless Smart and iHealth BG5L wireless Smart Gluco-Monitoring Systems consist of the BG5 and BG5L wireless Smart blood glucose meters, respectively, AGS-10001 Test Strips , sterile lancets, lancing device and the iHealth control solutions control solutions. (Control solutions provided are for Level 1, II, and III). iHealth BG5L uses Bluetooth 4.0 wireless radio technology; while iHealth BG5 uses Bluetooth 3.0 wireless radio technology. The iHealth BG5 and BG5L meters can display the test results and the test results can also be transmitted to an iPhone, iPod touch or iPad through blue tooth.

iHealth Gluco-Smart App is iOS- based software for use with the iHealth Align Glucose meter (BG1), iHealth BG5 meter, and iHealth BG5L meter. When used with these meters, iHealth Gluco-Smart App acts as a display and allows command and control of the meter. The App can transfer data from the device's memory, manage, and share the data.

Here's an analysis of the provided text, focusing on acceptance criteria and study details for the iHealth Gluco-Monitoring Systems:

The provided documents are a 510(k) premarket notification for the iHealth Align Gluco-Monitoring System, iHealth BG5 wireless Smart Gluco-Monitoring System, and iHealth BG5L wireless Smart Gluco-Monitoring System. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than undergoing extensive clinical trials typical of novel devices. Therefore, the "acceptance criteria" and "study" described are primarily focused on proving that the new devices perform comparably to the predicate device and meet relevant regulatory standards for glucose monitoring systems.

1. Table of Acceptance Criteria and Reported Device Performance

The documents do not explicitly state a table of "acceptance criteria" in the format of specific thresholds for metrics like sensitivity, specificity, or accuracy (e.g., within X% of a reference standard for Z% of readings). Instead, the acceptance criteria are implicitly tied to the performance characteristics of the predicate device and general regulatory expectations for glucose monitoring systems.

The performance is primarily summarized by stating that the new devices share key characteristics with the predicate and that "Software validation and user study has been performed to establish the performance, the functionality and the reliability characteristics of the new device."

Here's an attempt to infer and present the information in a table format based on the textual evidence:

2. Sample Size for the Test Set and Data Provenance

The document mentions "user study" but does not specify the sample size for any clinical or test set. It also does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the nature of a 510(k) for a glucose monitoring system, user studies often involve a diverse cohort to assess performance across various glucose levels and user demographics. However, these specific details are absent from the provided text.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention the use of experts to establish ground truth for a test set. For glucose monitoring systems, "ground truth" is typically established by comparing the device's readings against a highly accurate laboratory reference method (e.g., YSI glucose analyzer), rather than expert adjudication of images or clinical reports. The general term "user study" is used, which implies participants used the device and its performance was evaluated against a reference.

4. Adjudication Method for the Test Set

As the document does not describe a process involving experts to establish ground truth for a test set in the traditional sense, there is no mention of an adjudication method like 2+1 or 3+1. Performance is likely assessed against a laboratory reference.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The provided text does not describe an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. This type of study is more common in diagnostic imaging or clinical decision support AI devices where human interpretation is a key component. The iHealth devices are standalone glucose meters; while an app is involved, it primarily acts as a display and data management tool, not an AI for interpretation.

6. Standalone (Algorithm Only) Performance Study

Yes, implicitly. The core performance of the glucose measurement algorithm itself (i.e., the meter and test strip system) is evaluated. The 510(k) process for glucose meters typically requires studies demonstrating the accuracy of the device's readings against a laboratory reference method. Although the document uses the broad term "performance summary," this usually entails standalone accuracy data. The phrase "Software validation and user study has been performed to establish the performance, the functionality and the reliability characteristics of the new device" suggests that the device's ability to accurately measure glucose without a human in the interpretative loop was a key part of the validation.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used. However, for glucose monitoring systems, the ground truth is almost universally established using a laboratory reference method (e.g., a YSI glucose analyzer) that is considered the gold standard for glucose measurement.

8. Sample Size for the Training Set

The document does not mention a "training set" sample size. This is expected because the iHealth Gluco-Monitoring Systems, as described, do not appear to be AI/Machine Learning devices that require a "training set" in the context of learning algorithms. They are likely electrochemical biosensors with pre-defined algorithms for glucose calculation. The app primarily handles data display and management.

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

As there is no mention of a training set, there is no information on how its ground truth was established. The device likely relies on established physical and chemical principles of glucose measurement rather than a trained AI model.

Ask a specific question about this device