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The device is a wrist-worn digital monitor intended for use in measuring blood pressure and pulse rate in adult patients of ages 20 -70 with wrist circumference from 13.5 cm to 21.5 cm and BMI<40.

The Accurate Mini Non-invasive BPM is intended for spot-checking of adult patients in hospitals, clinics. long-term care, and home use. The measurement results are stored locally in the device.

The Accurate Mini Non-invasive BPM measures blood pressure based on Pulse Wave Transit Time (PWTT) obtained Local Pulse Wave Velocity (PWV) from dual Piezo Sensors and radial artery parameters from Near InfraRed Spectroscopy (NIRS) Optic sensor.

Accurate Mini Non-Invasive blood pressure monitor device is a small, lightweight, handheld device intended to measure and display blood pressure trending (Systolic and Diastolic) and spotcheck of pulse rate. Measurement is performed on the wrist at the radial artery. This blood pressure monitor uses the pulse wave transit time with the hemodynamic method to measure the systolic and diastolic blood pressure and pulse rate. This device serves to measure the systolic and diastolic blood pressure and pulse rate of adults in a non-invasive manner.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Device: Accurate Mini Non-invasive blood pressure monitor (AMB-001)

1. Table of Acceptance Criteria and Reported Device Performance

Note on ISO 81060-2:2018 Interpretation
The provided text presents the ISO 81060-2:2018 criteria in a somewhat consolidated manner.

• Mean Error: For blood pressure, ISO 81060-2:2018 requires a mean error of $\le \pm 5$ mmHg. The reported values (-0.24 and 0.35) are well within this.
• Standard Deviation:
  • Criterion 1 (mean difference $\pm$ standard deviation): This criterion requires the mean difference between the device and reference method, plus/minus its standard deviation, to be $\le 5 \pm 8$ mmHg. The text directly reports "SD (mmHg) < 8" and "4.85, 4.75", implying that the calculated SD meets this.
  • Criterion 2 (standard deviation of differences): This criterion requires the standard deviation of the differences between the device and reference measurements to be $\le 6.95$ mmHg. The text explicitly states "SD ($\textit{mmHg}$) $\le$ 6.95/6.95" and reports "2.33, 2.16", which are well within this.

Additional Performance Data for Specific Confounding Factors:

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

• Total Subjects for Clinical Validation Study: 367 subjects.
• Subjects for General Accuracy Study: 98 subjects.
• Subjects for Confounding Factors Study: 286 subjects (independently recruited for three groups: Fitzpatrick Scale Type 5 & 6, Age 51-70 years, Body Mass Index Grade I & II).
• Data Provenance: Not explicitly stated regarding country of origin, but the study was conducted according to international standards (ISO 81060-2:2018 and IEEE 1708:2014) and likely in a clinical setting ("hospitals, clinics, long-term care" are mentioned as intended use environments). The study appears to be prospective clinical validation.

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

• Not Applicable: This device is a non-invasive blood pressure monitor. The ground truth (reference measurements) are established using a validated reference device ("Welch Allyn DuraShock DS66 Trigger Aneroid Sphygmomanometer") operated by trained personnel, not through expert consensus/reading of images.

4. Adjudication Method for the Test Set

• Not Applicable: As this is a blood pressure measurement device validation, not an image interpretation task, a traditional adjudication method for multiple readers is not relevant. The ground truth is established by a reference device.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

• No: An MRMC comparative effectiveness study is not applicable for this type of device (blood pressure monitor). The study design focuses on comparing the device's measurements against a gold standard reference device, not on assessing human reader improvement with AI assistance.

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

• Yes (for the device's internal algorithm): The study evaluates the "Accurate Mini BPM" itself, which is a device containing an algorithm to measure blood pressure. Its performance is assessed independently against a reference device. The blood pressure monitor is a standalone measurement device; it does not involve a human-in-the-loop for measurement, although a human operates it.

7. The Type of Ground Truth Used

• Reference Device Comparison: The ground truth for blood pressure and pulse rate measurements was established by simultaneous or closely-timed measurements from a "Welch Allyn DuraShock DS66 Trigger Aneroid Sphygmomanometer," which serves as the gold standard reference device according to the relevant ISO standards (ISO 81060-2:2018).

8. The Sample Size for the Training Set

• Not Specified / Not Applicable (for this type of submission): For a traditional 510(k) for a device like a blood pressure monitor, details about the training set for the underlying algorithms (such as the PWTT calculations) are typically considered proprietary and are not usually provided in the public 510(k) summary. The clinical trial data provided is focused on validation (test set performance) against regulatory standards. The device's underlying algorithm is based on established hemodynamic principles (Moens-Korteweg equation) and sensor data, rather than being a deep learning model trained on a large dataset in the way an AI image analysis algorithm would be.

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

• Not Specified / Not Applicable (for this type of submission): As mentioned above, training set details are not typically disclosed for such devices in a 510(k) summary. The device's operation is based on physiological principles and sensor measurements, not a data-driven "ground truth" for training in the sense of expert-annotated data for machine learning.

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