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KD-7902 Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the wrist. The cuff circumference is limited to 6.1023 inches to 9.8425 inches.

KD-7902 Fully Automatic Electronic Blood Pressure Monitor is Non-invasive blood pressure measurement system for only one person each time. Based on oscillometric and silicon integrate pressure sensor technology, the device is used to monitor systolic, diastolic blood pressure and pulse rate which will be shown on a LCD with an electronic interface module. Buckling a cuff around the wrist, which cuff circumference is limited to 6.1023 inches to 9.8425 inches, the device can analyze the signals promptly and display the results. It has some other functions, such as 120 memory recall / 2 users and calculating average of last 3 readings.

Here's a breakdown of the acceptance criteria and the study information for the Andon Health Co., Ltd. KD-7902 Fully Automatic Electronic Blood Pressure Monitor, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The 510(k) summary only states that the device "meets the following standards" without providing specific performance metrics within each standard. For blood pressure monitors, compliance with ANSI/AAMI SP-10 typically implies accuracy requirements (e.g., mean difference and standard deviation between device and reference measurements) for both systolic and diastolic pressures.

2. Sample size used for the test set and the data provenance

The provided 510(k) summary does not explicitly state the sample size used for the test set in its summary for the KD-7902, nor does it specify the country of origin or whether the study was retrospective or prospective. It only indicates that the device meets the listed standards. Generally, for ANSI/AAMI SP-10, a minimum of 85 subjects is required for clinical validation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The 510(k) summary does not provide this information. For ANSI/AAMI SP-10 validation, ground truth is typically established by trained observers using a mercury sphygmomanometer or another validated reference device, following a specific protocol. These observers are trained to accurately read the reference device.

4. Adjudication method for the test set

The 510(k) summary does not provide this information. For clinical validation of blood pressure monitors, ground truth measurements are often obtained by two trained observers blinded to each other's readings and to the test device's readings, with protocols for resolving discrepancies (e.g., a third observer). This is a form of adjudication, but the specifics are not detailed here.

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. This device is a fully automatic electronic blood pressure monitor, not an AI-assisted diagnostic tool that involves human readers interpreting images or data. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

Yes, in essence. The testing against standards like ANSI/AAMI SP-10 for blood pressure monitors is a standalone performance assessment of the device's accuracy without a human "interpreter" in the loop. The device's algorithm performs the measurement and displays the result.

7. The type of ground truth used

For blood pressure monitors validated against ANSI/AAMI SP-10, the ground truth is typically expert-measured reference blood pressure readings obtained using a validated reference method (e.g., mercury sphygmomanometer) by trained observers.

8. The sample size for the training set

The 510(k) summary does not provide this information. As this is a traditional medical device (not an AI/ML device in the modern sense), the concept of a "training set" in the context of machine learning is not directly applicable. The device's "training" involves its design, calibration, and engineering to meet specific physiological measurement principles.

9. How the ground truth for the training set was established

Not applicable as there is no explicitly mentioned "training set" in the context of machine learning for this device. The device's underlying principles are based on established oscillometric measurement techniques and engineering, rather than being "trained" on a dataset in the way an AI algorithm would be. The design and calibration would have been based on extensive physiological knowledge and previous device performance data, but not a distinct "training set" as understood in current AI parlance.

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