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The wrist blood pressure monitor is used to carry out non-invasive measurement and monitoring of the arterial blood pressure values in adults. This allows you to quickly and easily measure your blood pressure, save the measured values and display the development and average values of the measured values taken. You are also warned of possible existing irregular heartbeat.

Digital Automatic Wrist Blood Pressure Monitor MD4300 (Subject Device) are non-invasive blood pressure measurement systems for use by medical professional or at home. It is designed to measure the systolic and diastolic blood pressure, and pulse rate of an individual in each measurement and then display the readings on a digital panel.

The Subject Device utilize the oscillometric methodology, in which an inflatable cuff is wrapped around the wrist of an individual, for blood pressure measurement. This means the monitor detects your blood's movement through your brachial artery and converts the movement into a digital reading.

The Subject Device equipped with Bluetooth transmission, which enable user to transfer the measurement record from the device to a mobile platform through Bluetooth. User can manage the measurement record by using the mobile application.

Here's a breakdown of the acceptance criteria and study information for the "Digital Automatic Wrist Blood Pressure Monitor MD4300" based on the provided text, structured as requested:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Clinical Test Set: 100 patients (60 males and 40 females).
• Data Provenance: Not explicitly stated (e.g., country of origin, prospective/retrospective). The "Clinical Test Summary" just states that "One hundred patients...were invited for the study."

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

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated. The ground truth was established using the "Standard auscultation method," which implies trained medical professionals (e.g., doctors or nurses) for manual blood pressure measurement.

4. Adjudication Method for the Test Set

• The text describes a comparison method: "Blood pressure measurements were repeated alternatively with the device and auscultation in the same arm according to the sequence in ISO 81060-2." This suggests a direct comparison between the device's reading and the auscultation reading for each patient. There is no mention of an adjudication process (e.g., 2+1 or 3+1) if discrepancies arose.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study described is a clinical accuracy study comparing the device against a reference standard (auscultation), not a comparative effectiveness study with human readers assisted by AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, the clinical test described is a standalone performance test of the device itself (algorithm and hardware) against the auscultation ground truth. There is no mention of "human-in-the-loop performance" or AI assistance in the context of this device.

7. The Type of Ground Truth Used

• The ground truth used was expert measurement/reference standard, specifically the "Standard auscultation method." This is considered a gold standard for non-invasive blood pressure measurement.

8. The Sample Size for the Training Set

• The document describes a clinical validation study (test set) of 100 patients. It does not mention a "training set" or any machine learning model training process for this device. The device likely relies on established oscillometric principles and calibration, not a data-driven machine learning model that requires a distinct training set.

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

• As no training set is mentioned or implied for a machine learning model, this question is not applicable. The device's operation is based on oscillometric methodology rather than AI training.

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