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The Microlife Automatic Blood Pressure Monitor, Model BP 3 AC1-1 P C i s a d evice intended to measure the systolic and diastolic blood pressure and pulse rate of an adult individual by using a non-invasive MAM (Microlife Average Mode) technique in which an inflatable cuff is wrapped around the upper arm.

The device detects the appearance of irregular heartbeat during measurement, and gives a warning signal with the reading once the irregular heartbeat is detected.

Microlife Automatic Blood Pressure Monitor, Model BP3AC1-1 PC, is designed to measure the systolic and diastolic blood pressure and pulse rate of an individual by using a non-invasive MAM (Microlife Average Mode) technique in which an inflatable cuff is wrapped around the Upper arm. Our method to define systolic and diastolic pressure is similar to the auscultatory method but uses an electronic capacitive pressure sensor rather than a stethoscope and mercury manometer. The sensor converts tiny alterations in cuff pressure to electrical signals, by analyzing those signals to define the systolic and diastolic blood pressure and calculating pulse rate, which is a well - known technique in the market called the "oscillometric method".

The device detects the appearance of irregular heartbeat during measurement, and the arrhythmia symbol " device can be used in connection with your personal computer (PC) running the Microlife Blood Pressure Analyzer (BPA) software. The memory data can be transferred to the PC by connecting the monitor via cable with the PC.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Microlife Automatic Blood Pressure Monitor, Model BP3AC1-1 PC:

Disclaimer: This device submission is from 2006. Modern medical device regulations and testing standards, especially for AI/ML-based devices, are significantly more rigorous than what is presented here. This analysis is based solely on the provided text, which describes a relatively straightforward, non-AI medical device by today's standards. The "AI" mentioned in the questions is not explicitly covered in the document as it pertains to advanced machine learning, but rather refers to the "software algorithm" for oscillometric measurements.

Based on the provided 510(k) summary for the Microlife Automatic Blood Pressure Monitor, Model BP3AC1-1 PC:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly state specific numerical acceptance criteria for blood pressure accuracy (e.g., mean difference and standard deviation compared to a reference standard). Instead, it states that the device's clinical performance "will remain unchanged" from its predicate device. The primary acceptance criteria appear to be compliance with relevant consensus standards and internal reliability tests.

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

• Sample Size: The document explicitly states: "Clinical performance of the modified device will remain unchanged; therefore another clinical test for the modified device, BP3AC1-1PC is not required." This means no new clinical test set (or patient sample) was used for this modified device. The submission relies entirely on the clinical performance of the predicate device (Microlife Automatic Blood Pressure Monitor, Model BP3BT0-AP).
• Data Provenance: Not applicable for a new clinical test, as none was performed. For the predicate device, the provenance is not detailed in this submission.

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

Not applicable. Since no new clinical tests were conducted for the BP3AC1-1 PC device, there was no new "ground truth" to establish for a test set. The submission relies on the established performance of the predicate device.

4. Adjudication Method for the Test Set

Not applicable, as no new clinical test set was used.

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 MRMC study was done. This device is a standalone blood pressure monitor.
• No "AI" in the modern sense (machine learning) is described. The "software algorithm" mentioned refers to the standard oscillometric method used to derive blood pressure and pulse rate. The submission does not involve human readers interpreting AI outputs or any AI assistance to human tasks.

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

Yes, the testing described is effectively a standalone performance assessment for the device itself. The device (algorithm + hardware) directly measures and displays blood pressure and pulse rate without human intervention in the measurement process (other than putting on the cuff and starting the device). The "clinical performance unchanged" statement refers to the device's standalone accuracy against a reference standard (likely auscultation) as established for the predicate device.

7. The Type of Ground Truth Used

For the predicate device's clinical validation (upon which this submission relies), the ground truth for blood pressure measurement would almost certainly have been established using expert auscultation simultaneous with the device's measurement, often with a double-blinded approach (e.g., using a Y-tube and two independent observers, with a third to break ties if necessary, and a mercury manometer as the reference standard). This standard methodology is typical for AAMI and BHS (British Hypertension Society) protocols.

8. The Sample Size for the Training Set

The concept of a "training set" as understood in modern machine learning is not applicable here. This device uses a pre-defined oscillometric algorithm, not a trainable AI model. Therefore, no distinct training set was used.

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

As above, the concept of a training set is not applicable. The underlying algorithm (oscillometric method) is based on well-established physiological principles and signal processing, not on iterative training with a labeled dataset. The parameters of the oscillometric algorithm would typically be refined through engineering and calibration rather than machine learning training.

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