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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 14cm-25cm.

Fully Automatic Electronic Blood Pressure Monitor (KD-743V, KD-743B, KD-752) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, and display the result on the LCD. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

This document is a 510(k) summary for a blood pressure monitor, not an AI/ML device. Therefore, the detailed information typically required for describing an AI/ML model's acceptance criteria and validation study (such as training/test set sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for AI models) is not provided in this document.

The document discusses the substantial equivalence of the "Fully Automatic Electronic Blood Pressure Monitor" (models KD-743V, KD-743B, KD-752) to predicate devices (KD-753, K183535 and KD-721, K163276). The "acceptance criteria" here refer to meeting established performance standards for non-invasive blood pressure monitors, rather than the performance metrics of an AI algorithm.

Here's a breakdown of what can be extracted and what information is not available from the provided text, in the context of your specific questions:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood pressure monitors are generally based on international standards like ISO 81060-2, which specifies requirements for the accuracy of automated non-invasive sphygmomanometers. The document states:

"Accuracy of the blood pressure monitors for the clinical test report was verified by meeting criteria 1 and criteria 2 of ISO 81060-2."

While the specific numeric criteria (e.g., mean difference and standard deviation of differences between device and reference measurements) are not explicitly listed in a table within this document, meeting ISO 81060-2 criteria 1 and 2 is the acceptance benchmark.

Detailed Information as Requested for AI/ML Studies:

1. A table of acceptance criteria and the reported device performance: See table above. This is based on NIBP standards, not AI performance metrics.

2. Sample sizes used for the test set and the data provenance:

   • The document refers to "clinical test report of KD-7961" and "clinical test report of KD-972" being used as reference for the subject devices. These reports verified accuracy by meeting ISO 81060-2 criteria.
   • ISO 81060-2 typically recommends a minimum of 85 subjects for validation. While not explicitly stated here for the specific test, it's implied that the reference clinical tests adhered to this.
   • Data Provenance: Not specified (e.g., country of origin). The document states the company is based in Tianjin, China.
   • Retrospective or Prospective: Not specified. Clinical validation studies for medical devices are typically prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable in the context of this device study. Ground truth for blood pressure monitors is established by simultaneously measuring blood pressure using a calibrated reference method (e.g., auscultation by trained observers) and the device under test. It's not a consensus-based reading like for imaging. ISO 81060-2 requires a minimum of three trained observers for reference measurements. Their specific qualifications (e.g., years of experience) are not detailed in this summary, beyond "trained observers."

4. Adjudication method for the test set: Not applicable in the AI/ML sense. For NIBP validation, the "adjudication" is the comparison of device readings against the mean of the reference observers' readings, as per ISO 81060-2. There's no consensus or 2+1/3+1 adjudication of images.

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 is a blood pressure monitor, not an AI-assisted diagnostic imaging device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable in the AI/ML sense. The device itself is "standalone" in that it performs the measurement. The "algorithm" here refers to the oscillometric method of blood pressure determination, not a separate AI algorithm that provides diagnostic interpretations.

7. The type of ground truth used: Reference blood pressure measurements obtained by trained observers using a reference method (e.g., auscultation), according to ISO 81060-2.

8. The sample size for the training set: Not applicable. This is a conventional medical device, not an AI/ML device that requires a distinct "training set" for model development. The "algorithm" for blood pressure measurement is based on established oscillometric principles and does not "learn" from a training set in the way an AI model does.

9. How the ground truth for the training set was established: Not applicable, as there's no training set for this type of device.

In summary: The provided document is a regulatory submission for a conventional medical device (blood pressure monitor), not an AI/ML product. Therefore, many of the detailed questions regarding AI/ML model validation are not addressed, as they are not relevant to this type of device. The "acceptance criteria" revolve around established performance standards for blood pressure measurement accuracy (ISO 81060-2).

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The Connect App for iHealth Next is intended for use in home settings as an aid for people to view vital signs which are measured by iHealth devices (noninvasive blood pressure monitor and pulse oximeter).

The Connect App for iHealth Next should not be used to either self-diagnose a disease state or exclude it. Please consult your physician if you find the reading is above a normal threshold.

The Connect App for iHealth Next is a software for using it with health devices, including iHealth medical devices such as Blood Pressure Monitor and pulse oximeter, etc. It can also be used with non-medical devices, such as active monitor, weight scale, etc.

The Connect App for iHealth Next can:

(1) Connect to blood pressure monitor and control the meter to complete measurement, and the data can be transferred to the Connect App for iHealth Next;

(2) Connect to pulse oximeter and transfer data from the pulse oximeter to the Connect App for iHealth Next.

This document describes the Connect App for iHealth Next, a data management software. No specific acceptance criteria for diagnostic or prognostic performance are described in the provided text, as this device primarily focuses on data management and control of other medical devices. Therefore, a comprehensive table of acceptance criteria and reported device performance related to diagnostic accuracy cannot be generated from this submission.

However, the document does describe validation activities for other aspects of the device:

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

As mentioned above, no specific diagnostic or prognostic acceptance criteria are provided in the text. However, a "Performance summary" section mentions non-clinical tests performed:

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

• Usability Study: 20 lay users and 20 professionals participated in the usability study. The provenance is not explicitly stated but implies testing in "their actual use environment such as home, hospital, clinic and office etc."
• For software validation and wireless coexistence, specific sample sizes (e.g., number of test cases) are not provided.

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

Not applicable as the device is data management software, and the "ground truth" for the usability study would be user feedback and successful task completion, not diagnostic ground truth established by medical experts for a clinical condition. The participants were "20 lay users and 20 professionals," with no further qualification details provided.

4. Adjudication method for the test set

Not applicable. The usability study involved observing user interaction and feedback, not a traditional adjudication process for clinical endpoints.

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 performed or described. This device is data management software, not an AI-powered diagnostic tool requiring human reader comparison.

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

The device is software for managing data and controlling medical devices. Its "performance" revolves around software functionality, usability, and wireless connectivity, which were tested in a standalone manner (without continuous human interaction to refine its core functions) but with human users evaluating its user-facing aspects.

7. The type of ground truth used

• Software Validation: The "ground truth" for software validation would be adherence to specified requirements and established software engineering best practices, verified through testing against expected outputs.
• Usability Study: The "ground truth" was defined by successful task completion, understanding of warnings/precautions, and overall user experience as observed and reported by participants (lay users and professionals) against predetermined critical tasks and usability goals.

8. The sample size for the training set

Not applicable. This device is software for data management and device control, not a machine learning model that requires a training set in the conventional sense.

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

Not applicable (see point 8).

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KD-721 and KD-723 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 14cm-25cm.

KD-721 and KD-723 Fully Automatic Electronic Blood Pressure Monitor is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, and display the result on the LCD. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

The provided text describes the acceptance criteria and the study for the KD-721 and KD-723 Fully Automatic Electronic Blood Pressure Monitor.

1. Table of Acceptance Criteria and Reported Device Performance:

The document refers to compliance with particular standards, which implicitly define the acceptance criteria for certain performance aspects. The device characteristics are compared to those of a predicate device (iHealth View BP7S Wireless Blood Pressure Wrist Monitor, K152379) and a previous device (KD-972, K121470).

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

The document states that the clinical test report of KD-972 (cleared in 2012 as K121470) was used for evaluating KD-721/723. This implies retrospective use of clinical data.
The document states that KD-972 conformed to ANSI/AAMI/ISO 81060-2:2009 & 2013. These standards typically outline the requirements for clinical validation studies for automated sphygmomanometers, which involve a specific number of subjects. However, the exact sample size for the test set is not explicitly stated in the provided text. The data provenance is not specified beyond being from a previous device's clinical trial conforming to international standards (ANSI/AAMI/ISO 81060-2), which generally requires multi-center data from diverse populations.

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. Clinical validation studies for blood pressure monitors generally involve trained observers (often medical professionals) to perform reference measurements.

4. Adjudication method for the test set:

The document does not specify the adjudication method used for the test set.

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

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

Yes, a standalone performance evaluation was done through the reference to the KD-972 clinical test report, which conformed to ANSI/AAMI/ISO 81060-2:2009 & 2013. This standard specifically outlines the requirements for evaluating the accuracy of automated sphygmomanometers, which inherently tests the algorithm's performance in measuring blood pressure values.

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

The ground truth for blood pressure monitors in clinical validation studies conforming to ANSI/AAMI/ISO 81060-2 is typically established through auscultatory measurements performed by trained observers using a mercury sphygmomanometer or an equivalent reference device, often in a double-blinded protocol to minimize bias. This is a form of expert reference measurement.

8. The sample size for the training set:

The document states that the algorithm version (BPM-WAU V2.0-201109) is the same for KD-721/723 and the previously cleared KD-972. This implies that the algorithm was trained prior to the KD-972 clearance (K121470 in 2012). The sample size for the training set is not provided in this document, as the focus is on the clinical validation of the device using an existing algorithm.

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

Given that the algorithm is based on "oscillometric and silicon integrates pressure sensor technology" and is a "fully automatic" device, the ground truth for its original training would have been established through a combination of simulated data and clinical data where reference blood pressure measurements (e.g., auscultatory method by trained experts) were used to optimize the algorithm's performance in detecting systolic, diastolic, and pulse rate. The specific details of the training data and ground truth establishment are not provided in this document.

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