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The Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3NF 1-2B is a device intended to measure the systolic and diastolic blood pressure, pulserate of an adult individual by using a non-invasive oscillometric technique in which an inflatable cuff is wrapped around the single 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.

The 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. The device can also be used in connection with smart phone running the APP. The memory data can be transferred to the smart phone via Bluetooth.

Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3NF1-2B is designed to measure systolic and diastolic blood pressure, pulse rate of an individual by using a non-invasive technique in which one inflatable cuff is wrapped around the single upper arm. Our method to define systolic and diastolic pressure is similar to the auscultatory method but use two resistive pressure sensors rather than a stethoscope and mercury manometer. The sensors convert 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 symbol " " is displayed with the reading. The device can be used in connection with your personal computer (PC) running the software. The memory data can be transferred to the PC by connecting the monitor with the PC via cable. The device can also be used in connection with smart phone running the APP. The memory data can be transferred to the smart phone via Bluetooth.

The provided text does not contain detailed information about the acceptance criteria and the study that proves the device meets those criteria in the format requested.

The document is a 510(k) premarket notification for a blood pressure monitor, focusing on demonstrating substantial equivalence to a predicate device. It briefly mentions non-clinical and clinical tests but does not provide the specific data points requested in the prompt.

Here's a breakdown of what is mentioned, and what is missing:

What is mentioned:

• Non-Clinical Tests: The document states that testing was conducted in accordance with FDA guidance and various national and international standards (e.g., IEC 60601-1, AAMI/ANSI SP10, EN 1060-1, ISO 14971, AAMI/ANSI/IEC 80601-2-30). These standards typically define performance requirements and test methods for electrical safety, EMC, and blood pressure measurement accuracy.
• Clinical Tests: It explicitly states that repeat clinical testing in accordance with ANSI/AAMI IEC81060-2 for the subject device BP3NF1-2B was not necessary. The justification is that the subject device is technically identical to the predicate device (BP3MC1-PC) in terms of its blood pressure measurement algorithm and program codes, meaning their performance should be identical.
• Predicate Device: The clinical performance of the device is inferred from the performance of its predicate devices, specifically "Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3MC1-PC, K061471". While the standards listed (like AAMI/ANSI SP10 and AAMI/ANSI/IEC 80601-2-30) define accuracy requirements, the document does not report the specific performance metrics of the predicate device or the subject device against these requirements.
• Sample Size (Clinical): Not applicable for the subject device, as no new clinical study was performed.
• Ground Truth (Clinical): Not directly applicable for a new clinical study. The implied ground truth would be from the clinical studies conducted for the predicate device, likely using auscultation by trained observers.

What is missing from the provided text to fulfill your request:

1. A table of acceptance criteria and the reported device performance: This is the most crucial missing piece. While standards like AAMI/ANSI SP10 and IEC 81060-2-30 are mentioned, which contain accuracy criteria (e.g., mean difference and standard deviation between device and reference measurements), the document does not report the actual performance of the device against these criteria. It only states that the device "met all relevant requirements."
2. Sample sized used for the test set and the data provenance: Not provided, as no new clinical study was documented for this submission. This information would be found in the original clinical validation for the predicate device(s).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided. This information would be part of the predicate device's clinical validation report.
4. Adjudication method: Not provided. This information would be part of the predicate device's clinical validation report.
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: Not applicable. This device is a standalone blood pressure monitor, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the device itself is a standalone algorithm for blood pressure measurement. However, the specific performance metrics are not reported here.
7. The type of ground truth used: Implied to be auscultatory reference measurements by trained observers for the predicate device's clinical validation, as per standard blood pressure validation protocols (e.g., AAMI/ANSI/ISO 81060-2).
8. The sample size for the training set: Not applicable. This document pertains to the evaluation of a medical device, not the development of a machine learning model with a distinct training set. The "algorithm" here refers to the oscillometric measurement method.
9. How the ground truth for the training set was established: Not applicable.

In summary, the document leverages the concept of substantial equivalence by arguing that no new clinical data is needed because crucial components, especially the measurement algorithm, are identical to a previously cleared predicate device. Therefore, the detailed acceptance criteria and performance data for the subject device are not presented, but are implicitly covered by the predicate device's prior approval against relevant standards.

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The Wrist Watch Blood Pressure Monitor, Model BP3NN1-3E is a device intended to measure the systolic and diastolic blood pressure and pulse rate of an adult individual by using a non-invasive oscillometric technique in which an inflatable cuff is wrapped around the wrist for a circumference range from 13.5 to 21.5cm.

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 Wrist Watch Blood Pressure Monitor, Model BP3NN1-3E is designed to measure systolic and diastolic blood pressure and pulse rate of an individual by using a non-invasive technique in which an inflatable cuff is wrapped around the wrist. Our method to define systolic and diastolic pressure is similar to the auscultatory method but uses a semiconductor 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 can detect electrical signals while inflating . And the device has Irregular Heartbeat Detection (IHD) function. It detects the appearance of irregular heartbeat during measurement and the irregular heart beat symbol "MA" is displayed on the LCD screen if any irregular heart beat signal has been detected. In addition, the device has traffic light function.

This document describes the Microlife Wrist Watch Blood Pressure Monitor, Model BP3NN1-3E, and its substantial equivalence to predicate devices, particularly concerning its performance characteristics.

Here's an analysis of the provided information regarding acceptance criteria and supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to predicate devices, rather than explicitly stating acceptance criteria and then presenting a direct comparison. However, the regulatory standards cited implicitly define the acceptance criteria for non-invasive blood pressure measurement devices.

The key performance aspect mentioned is the blood pressure measurement accuracy and the Irregular Heartbeat Detection (IHD) function.

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

The document explicitly states: "Repeat clinical testing in accordance with the standard ANSI/AAMI IEC81060-2 for the subject device BP3NN1-3E is therefore not necessary as clinical testing results were not affected by the changes to the subject modified device."

Therefore, a new, separate clinical test set for the BP3NN1-3E model was not used for demonstrating blood pressure measurement accuracy. The reliance is on the clinical data for the predicate device BP3MO1-3P.

• Sample Size for Test Set: Not applicable for the BP3NN1-3E's blood pressure measurement accuracy. The performance is inferred from the predicate device (BP3MO1-3P). The document does not specify the sample size for the predicate's original clinical study, nor its data provenance.
• Data Provenance: Not applicable for a new clinical study of the BP3NN1-3E. For the predicate device, it is not specified in this document.

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

This information is not provided for the BP3NN1-3E device as a new clinical study for blood pressure measurement accuracy was deemed unnecessary. For the predicate device's original clinical validation, this detail is absent from the provided text.

4. Adjudication Method for the Test Set

This information is not provided for the BP3NN1-3E device as a new clinical study for blood pressure measurement accuracy was deemed unnecessary. For the predicate device, this detail is absent from the provided text.

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

This is not applicable. The device described is a standalone blood pressure monitor, not an AI-assisted diagnostic tool that would involve human readers or comparative effectiveness studies of the type described.

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

Yes, a standalone evaluation of the device's performance is implied. The document states that the "Microlife Wrist Watch Blood Pressure Monitor, Model BP3NN1-3E is designed to measure systolic and diastolic blood pressure and pulse rate of an individual by using a non-invasive technique". The performance is based on the device's algorithms and hardware, without requiring human interpretation for the primary blood pressure measurement task.

7. The Type of Ground Truth Used

For blood pressure measurement devices, the ground truth for clinical accuracy studies (such as those done for the predicate device) typically involves simultaneous measurements by trained observers using a validated reference method (e.g., auscultation with a mercury sphygmomanometer or validated electronic reference device), following a rigorous protocol (like the ANSI/AAMI/ISO 81060-2 standard).

8. The Sample Size for the Training Set

This information is not provided. The document does not discuss a "training set" in the context of machine learning. The device uses an "oscillometric method" with "measurement algorithm and its program codes" which, while involving signal processing, is described as a "well-known technique" rather than a system trained on a specific dataset in the modern AI sense.

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

This information is not provided as the concept of a "training set" in the context of an AI/machine learning model is not explicitly discussed for this device. The underlying oscillometric method and algorithms would have been developed and validated over time based on physiological principles and empirical data, but not necessarily through a formal "training set" with established ground truth as understood in current AI development.

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The Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3MW1-4X(R) is a device intended to measure the systolic and diastolic blood pressure, pulse rate of an adult individual by using a non-invasive oscillometric technique in one inflatable cuff is wrapped around the single upper arm.

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

The device can accurately measure blood pressure in pregnant patients including those with known or suspected preeclampsia.

The 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.

Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3MW1-4X(R) is designed to measure systolic and diastolic blood pressure, pulse rate of an individual by using a non-invasive technique in which one inflatable cuff is wrapped around the single upper arm. Our method to define systolic and diastolic pressure is similar to the auscultatory method but use two resistive pressure sensors rather than a stethoscope and mercury manometer. The sensors convert 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 can accurately measure blood pressure in pregnant patients including those with known or suspected preeclampsia.

The device detects the appearance of irregular heartbeat during measurement, and the symbol " " "is displayed after the measurement. In addition, the device can be used in connection with your personal computer (PC) running the software. The memory data can be transferred to the PC by connecting the monitor with the PC via cable.

Here's a breakdown of the acceptance criteria and study information for the Microlife Upper Arm Automatic Digital Blood Pressure Monitor, Model BP3MW1-4X(R), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions clinical validation reports but does not explicitly state the sample size used for the clinical test set.

• Sample Size (Test Set): Not explicitly stated in the provided text for the clinical validation or other tests.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study is described as a "clinical validation report," implying prospective clinical testing.

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

This information is not provided in the text. The document refers to the ANSI/AAMI/ISO 81060-2:2009 and AAMI SP10 protocols for blood pressure measurement validation, which typically involve a specific number of observers (experts) for reference measurements. However, the exact number and qualifications of these experts are not detailed in this summary.

4. Adjudication Method for the Test Set

This information is not provided in the text. The AAMI protocols typically have specific requirements for how reference measurements are compared and adjudicated, but the summary does not elaborate on this for this specific study.

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

There is no indication of an MRMC comparative effectiveness study involving human readers or AI in this document. This device is an automatic digital blood pressure monitor, not an AI-assisted diagnostic imaging or interpretation tool.

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

Yes, the primary clinical testing described is a standalone validation of the device's accuracy in measuring blood pressure and detecting irregular heartbeats, according to the ANSI/AAMI/ISO 81060-2:2009 protocol. This protocol evaluates the device's performance without human interpretation or assistance beyond the typical operation of a blood pressure monitor.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the blood pressure accuracy validation, the ground truth would be established through reference measurements taken by trained observers using a calibrated sphygmomanometer (or similar reference method) according to the ANSI/AAMI/ISO 81060-2:2009 protocol. This often involves simultaneous or nearly simultaneous measurements with the test device and a highly accurate reference method. For "irregular heartbeat detection," the ground truth would likely be established by a physician's or cardiologist's diagnosis using an ECG or other standard diagnostic methods.

8. The Sample Size for the Training Set

This device is based on the "oscillometric method," which is a well-known and established technique for blood pressure measurement. While software is involved in analyzing the signals, the document does not suggest a machine learning model that would require a distinct "training set" in the modern sense (i.e., for deep learning or AI). The software algorithm is described as using the "well-known oscillometric method." Therefore, the concept of a separate "training set" for an AI model, as typically understood, does not apply in this context.

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

As explained under point 8, the concept of a "training set" for an AI model with a distinct ground truth establishment method is not applicable to this device. The oscillometric method itself is based on established physiological principles and signal analysis techniques, not a data-driven machine learning training process described in the document.

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