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This device is intended to measure the blood pressure and pulse rate on upper arms of adults.

The Digital Blood Pressure Monitor is Arm-type fully automatic Digital Blood Pressure Monitor and used the oscillometric method for non-invasive measurement of blood-pressure at home.

The equipment is non-transit-operable, portable equipment, not used at the conditions of patient movement in normal use. The unit is powered by internal dry battery.

Digital Blood Pressure Monitor is based on pressure vibration method. Blood pressure cuff use the air pump to inflate, then the arteries are extruded by the cuff with pressure. Pressure sensor collects the pressure in the cuff, and then converts it to digital signal to the CPU. Then the software calculates the systolic and diastolic blood pressure and pulse rate.

Digital Blood Pressure Monitor, JDS-189, JDS-500A, JDS-500E, JDS-600, JDS-700, JDS-703, JDS-704, JDS-800, BM 20 in term of intended use measurement method, structure, and specifications are totally same, only the appearance, memory space has difference.

The provided document is a 510(k) summary for a Digital Blood Pressure Monitor, aiming to demonstrate its substantial equivalence to a predicate device. It includes information on the device's intended use, technology, and applied standards, as well as testing data.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and the Reported Device Performance

Note: The document states that "Laboratory testing was conducted to validate and verify that Digital Blood Pressure Monitor met all requirements of related international standards," and specifically lists the performance requirements for pressure and pulse accuracy. It also notes "clinical study has been evaluated according to ANSI/AAMI/ISO 81060-2." This implies that the device achieved accuracy within the limits specified by the accuracy requirement, which are typical for blood pressure monitors.

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

The document states: "Clinical study has been evaluated according to ANSI/AAMI/ISO 81060-2."

• Sample Size for Test Set: The document does not explicitly state the sample size used for the clinical study that followed ANSI/AAMI/ISO 81060-2. This standard generally requires a minimum number of subjects (e.g., typically 85 subjects with specific age and blood pressure distribution) for accuracy validation.
• Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. Given the submitter's location (SUZHOU SUNSPIRIT CO., LTD., P. R. China), it is highly probable the study was conducted there. It is typically a prospective study to meet the requirements of ISO 81060-2.

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

The document mentions "Clinical study has been evaluated according to ANSI/AAMI/ISO 81060-2," but it does not provide details on the number of experts, their qualifications, or how ground truth was established within the context of that clinical study. For blood pressure measurement, the ground truth (reference measurements) would typically be established by trained observers using a calibrated sphygmomanometer following a standardized protocol, rather than "experts" in the sense of physicians establishing a diagnosis.

4. Adjudication Method (for the test set)

The document does not specify an adjudication method. In the context of blood pressure device validation, and specifically for ISO 81060-2, adjudication would usually involve comparing the device's readings against simultaneously obtained reference readings. The standard outlines specific procedures for taking these reference readings (e.g., using two trained observers auscultating simultaneously).

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

The document does not mention or imply that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. MRMC studies are typically used to assess the impact of AI algorithms on human reader performance, which is not applicable to a digital blood pressure monitor that performs automatic measurements.

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

Yes, a standalone performance evaluation was done. The device itself is an "Arm-type fully automatic Digital Blood Pressure Monitor" that uses an "oscillometric method" to calculate systolic and diastolic blood pressure and pulse rate automatically. The clinical study according to ANSI/AAMI/ISO 81060-2 evaluates the accuracy of this automatic measurement against a reference standard, which is a standalone assessment of the device's algorithm.

7. The Type of Ground Truth Used

The ground truth used for the clinical evaluation, as per ANSI/AAMI/ISO 81060-2, would be simultaneously measured blood pressure readings obtained by trained observers using a reference sphygmomanometer (typically mercury or a validated equivalent) following a strict protocol. This method is often referred to as "reference measurement" or "expert auscultation" and is the gold standard for non-invasive blood pressure device validation.

8. The Sample Size for the Training Set

The document does not provide any information about a training set since this typically refers to data used to train a machine learning model. While the device utilizes an oscillometric method with software, it's not explicitly stated if it employs machine learning that would require a separate training dataset. The "clinical study" mentioned refers to the validation study for regulatory approval, not a training phase.

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

As no training set is explicitly mentioned in the context of machine learning, there is no information provided on how ground truth for a training set was established. The oscillometric method relies on established physiological principles and algorithms, rather than typically being "trained" in the modern machine learning sense with a distinct training dataset and corresponding ground truth.

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