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The U80 Series Upper Arm Electronic Blood Pressure Monitor is intended to measure the systolic and diastolic blood pressure as well as the pulse rate of adult person via non-invasive oscillometric technique in which an inflatable cuff is wrapped around the upper arm. It can be used at medical facilities or at home. The intended upper arm circumference is 22-36cm.Suitable for adults who over the age of 12.

The proposed device, U80 Series Electronic Blood Pressure Monitor, is a battery driven automatic on-invasive blood pressure monitor. It can automatically complete the inflation and measurement, which can measure systolic and diastolic blood pressure as well as the pulse rate of adult person at upper arm within its claimed range and accuracy via the oscillometric technique. User can select the unit of the measurement: mmHg or kPa.

All the models included in this submission follow the same measurement principle and same specifications. The main differences are appearance, data storage and time display. These three differences will not affect the safety and effectiveness of the device.

The provided text describes a 510(k) premarket notification for a medical device, the "Upper Arm Electronic Blood Pressure Monitor U80 Series." This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting a full clinical trial to prove efficacy and safety from scratch.

Therefore, the document does not contain the detailed information typically associated with a study proving that a device meets specific acceptance criteria for AI/ML performance (e.g., number of experts, adjudication methods, MRMC studies, ground truth establishment for AI training/test sets). Instead, it relies on bench testing and comparison to an existing device.

Here's an analysis based on the available information and an explanation of why certain information requested is not present:

Context: The device is a non-invasive blood pressure monitor. The "acceptance criteria" discussed here are primarily related to meeting recognized performance standards for blood pressure measurement and demonstrating equivalence to a predicate device, not necessarily AI/ML performance criteria.

1. A table of acceptance criteria and the reported device performance

The document presents a "Substantially Equivalent Comparison" table (Table III-1) which serves as the primary way the device's performance is compared against regulatory and predicate device benchmarks. The "acceptance criteria" are implicitly defined by the predicate device's performance and relevant standards.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document primarily relies on bench tests and compliance with recognized standards for its performance evaluation, rather than a clinical "test set" in the context of AI/ML validation involving patient data.

The standards cited include:

• ANSI/AAMI/IEC 80601-2-30:2009 "Medical electrical equipment Part 2-30: Particular requirements for the basic safety and essential performance of automated noninvasive sphygmomanometers."
• ANSI/AAMI/ISO 81060-2:2009 "Non-invasive sphygmomanometers - Part 2: Clinical validation of automated measurement type."

These standards outline clinical testing requirements, including sample sizes for validation studies for blood pressure monitors. For instance, ISO 81060-2 typically requires a certain number of subjects (e.g., usually around 85 subjects with specific age and BP distribution) for clinical validation. However, the specific sample size used in the company's validation study or its provenance for this specific submission is not explicitly detailed in the provided 510(k) summary. It is stated that "Bench tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device. The test results demonstrated that the proposed device complies with the following standards." This implies the tests mandated by these standards were performed, but the summary does not include the raw results or the clinical trial specifics.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable and therefore not present in the document. The device is a blood pressure monitor, not an AI/ML diagnostic imaging device that requires radiologist interpretations for ground truth. The "ground truth" for a blood pressure monitor is established through a reference measurement method (e.g., auscultation by trained observers with mercury sphygmomanometers) as per the mentioned ISO/AAMI standards. The document does not detail the specifics of such clinical validation for this particular submission, only that the device "complies" with the standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not applicable as it pertains to expert consensus on diagnostic images, not a standard blood pressure measurement device.

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 information is not applicable. This question refers to AI-assisted diagnostic tools. The device is an automated blood pressure monitor; there are no "human readers" interpreting its output that would be assisted by AI in this context.

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

The device itself is a "standalone" automated blood pressure monitor in that it measures blood pressure directly via an oscillometric technique. Its performance is evaluated purely on its ability to accurately measure BP, as detailed by the standards it claims compliance with (e.g., accuracy of ±3 mmHg). There isn't an "algorithm only" component separate from the integrated device for this type of product that would typically necessitate such a study as posed in the context of complex AI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For blood pressure monitors, the "ground truth" for clinical validation is typically established through simultaneous auscultatory measurements performed by trained human observers using a validated reference method (e.g., mercury sphygmomanometer or another agreed-upon reference device), following the protocols outlined in standards like ANSI/AAMI/ISO 81060-2. The exact specifics of how this ground truth was established for this specific device's compliance are not detailed in the provided 510(k) summary, beyond stating compliance with the standard that mandates such methods.

8. The sample size for the training set

This information is not applicable. This is not an AI/ML device that undergoes training on a large dataset in the way a deep learning model would. Its "training" or development process would involve traditional engineering and calibration.

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

This information is not applicable for the reasons stated in point 8.

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