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HL858GB uses the oscillometric method to automatically measure systolic and diastolic blood pressure as well as heart rate. The measurement position is at human being's arm. All values can be read out in one LCD panel. The device is designed and recommended for use by people over the age of 18 with arm circumference ranging from approx.9 inches to 17 inches (23 cm to 43 cm) and for home use.

When the device detects the appearance of irregular heartbeats during measurement, an indicated symbol will appear with measuring readings. And the Near Field Communication (NFC) function allows user to transmit measuring result from the blood pressure monitor into NFC-enabled device simply and safely.

Besides, BP Category Indicator feature will judge blood pressure results into six levels based on WHO (World Health Organization) classification with corresponding bar segment on the edge of screen.

HL858GB uses the oscillometric method to automatically measure systolic and diastolic blood pressure as well as heart rate. The measurement position is at human being's arm. All values can be read out in one LCD panel. The device is designed and recommended for use by people over the age of 18 with arm circumference ranging from approx.9 inches to 17 inches (23 cm to 43 cm) and for home use.

The device will display a symbol - worth , to indicate the detection of irregular heartbeat rhythm as defined as a rhythm is more than or less than 25% from the average heartbeat intervals during the measurement. Additionally, after measurement, the BP Category Indicator function will show the information with the readings on the screen for the user tracking their blood pressure level. Furthermore, the Near Field Communication (NFC) function allows user to transmit measuring results from the blood pressure monitor into NFC-enabled device simply and safely.

The provided text describes the regulatory clearance for the "Full Automatic (NIBP) Blood Pressure Monitor, Model HL858GB" and includes information about its performance validation.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

Study Proving Acceptance Criteria:

The primary study proving the device's accuracy in measuring blood pressure is a clinical investigation conducted according to ISO 81060-2: Second Edition 2013-05-01 "Non-invasive sphygmomanometers- Part 2: Clinical validation of automated measurement type."

2. Sample Size Used for the Test Set and Data Provenance:

• Sample size: 85 subjects.
• Data Provenance: Not explicitly stated whether retrospective or prospective, nor the country of origin. However, clinical validation according to ISO 81060-2 typically involves prospective data collection.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

The document does not explicitly state the number of experts or their qualifications for establishing ground truth in the clinical validation. However, ISO 81060-2 (the standard cited) mandates specific procedures for reference measurements, usually involving multiple trained operators taking auscultatory measurements simultaneously or sequentially, with their measurements serving as the reference. These operators would be considered the "experts" for ground truth in this context, but their number and specific qualifications (e.g., years of experience as clinicians or trained technicians) are not detailed in this summary.

4. Adjudication Method for the Test Set:

The document does not explicitly describe an adjudication method for the test set. In the context of ISO 81060-2, the "ground truth" (reference measurements) are typically established by averaging or reconciling readings from two or three trained observers, which acts as a form of adjudication. The standard itself outlines how discrepancies between observers should be handled.

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:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This document pertains to a blood pressure monitor, which is a standalone measurement device, not an AI-assisted diagnostic tool that human readers would interpret. Therefore, the concept of "human readers improving with AI vs. without AI assistance" is not applicable here.

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

Yes, a standalone performance study was done. The clinical investigation performed according to ISO 81060-2 evaluates the automated blood pressure monitor's accuracy by comparing its readings directly against a validated reference method (typically auscultatory measurements by trained observers). This assesses the algorithm's performance in determining blood pressure values automatically without human intervention in the measurement process itself, beyond initiation and cuff placement.

7. The Type of Ground Truth Used:

The ground truth for the clinical investigation (ISO 81060-2) is established by expert reference measurements, typically simultaneous or sequential auscultatory measurements performed by trained observers using a mercury sphygmomanometer or another validated reference device. This is a form of expert consensus if multiple observers are used and their readings reconciled.

8. The Sample Size for the Training Set:

The document does not provide information on a training set size. Blood pressure monitors using the oscillometric method are typically based on established algorithms and hardware components. While there might be internal algorithm development and calibration, the provided regulatory submission focuses on the clinical validation of the final product as per ISO standards rather than detailing a machine learning training process.

9. How the Ground Truth for the Training Set was Established:

As no specific training set is mentioned in the context of an AI/ML model, the establishment of ground truth for a training set is not applicable here. The device's "training" in a broad sense would be the development and calibration of the oscillometric algorithm against various blood pressure profiles, which is a standard engineering process for such devices rather than a machine learning training paradigm described for AI.

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