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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.

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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, 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 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 Align Gluco-Monitoring System(BG1) consist of blood glucose 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 the iHealth Align Gluco-Monitoring system(BG1) , a compatible Android or iOS mobile device with the necessary mobile application installed is required.

The provided text describes the iHealth Align Gluco-Monitoring System (BG1) and its substantial equivalence to a predicate device. However, it does not include detailed acceptance criteria or a specific study proving the device meets those criteria in the format requested.

The document is a 510(k) summary for a glucose monitoring system, focusing on demonstrating substantial equivalence to a previously cleared device. It highlights the technological characteristics and intended use. While it mentions performance characteristics like measurement range and test time, it doesn't present a table of acceptance criteria with corresponding device performance for a specific study.

Therefore, I can extract information related to the device's characteristics and the submission's intent, but I cannot fulfill all sections of your request directly from the provided text as the specific clinical study data (including sample sizes, ground truth establishment, expert qualifications, and adjudication methods for acceptance criteria) is not present.

Here's a breakdown of what can be extracted and what cannot:

Information that CANNOT be extracted from the provided text:

• A table of acceptance criteria and the reported device performance because the document focuses on demonstrating substantial equivalence through technological comparison rather than presenting the results of a primary clinical validation study against predefined acceptance metrics.
• Sample sizes used for the test set and data provenance for a specific clinical validation study.
• Number of experts used to establish the ground truth and their qualifications.
• Adjudication method for the test set.
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done or its effect size.
• Whether a standalone (algorithm only) performance study was done. (This device is a physical glucose meter, not an AI algorithm.)
• The sample size for the training set. (This is a medical device, not an AI algorithm that typically has a "training set" in the machine learning sense.)
• How the ground truth for the training set was established.

Information that CAN be extracted or inferred:

1. Acceptance Criteria and Reported Device Performance:

While a formal "acceptance criteria" table is not provided, the document lists key performance characteristics assumed to meet regulatory expectations. The predicate device's performance, which the new device is compared against, indirectly sets the "acceptance criteria" for substantial equivalence.

7. Type of Ground Truth Used:
The device measures chemical properties (glucose levels). The "ground truth" for such devices is typically established through a laboratory reference method (e.g., using a YSI glucose analyzer) on the same blood sample. While not explicitly stated, this is the standard for glucose meter validation.

The 510(k) summary explicitly states that the submission aims to demonstrate that "these small differences [referring to the expanded compatibility with Android devices] do not raise any new questions of safety and effectiveness," thus proving its substantial equivalence to the predicate device. This implies that the device performance for glucose measurement itself is considered equivalent to the predicate, which would have undergone its own rigorous testing against a reference standard.

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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.

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