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Model CH-4532 Blood Pressure Meter is intended to be used for oscillometric measurement of systolic and diastolic blood pressure and pulse and is intended to be sold at a retail shop and to be used for checking personal health condition at home, and not primarily under the order or direction of a physician.

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The provided document is a 510(k) premarket notification letter from the FDA to Citizen Systems Japan Co., Ltd. for their Model CH-4532 Blood Pressure Meter. This document primarily focuses on regulatory approval and does not contain the detailed study information required to answer your questions about acceptance criteria, device performance, training, and test set specifics for an AI/ML medical device.

The FDA 510(k) approval process for non-AI devices like this blood pressure meter typically relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than extensive clinical trials with detailed AI performance metrics.

Therefore, most of the specific questions you've asked cannot be answered directly from the provided text. I will indicate where information is missing or not applicable based on the document's content.

Acceptance Criteria and Device Performance (Not Available in Document)

The document does not detail specific performance acceptance criteria or report device performance data from a pivotal study. The FDA letter states the device is "substantially equivalent" to predicate devices, implying that its performance meets the regulatory bar set by those predicate devices, but the specific metrics are not elaborated here.

Table of Acceptance Criteria and Reported Device Performance:

Study Details (Not Available for AI/ML Device, General for this device)

Since this is a conventional blood pressure monitor and not an AI/ML device, many of the questions related to AI/ML specific study design (e.g., test set provenance, ground truth establishment, training set details) are not applicable.

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

• Result from Document: Not applicable as this is a non-AI device. The FDA letter does not contain information on the clinical study's sample size or data provenance for demonstrating blood pressure measurement accuracy.
• General Context: For blood pressure monitors, accuracy studies are typically conducted according to international standards (e.g., ISO 81060-2) which specify subject inclusion criteria, measurement protocols, and statistical analysis methods. These studies usually involve a specific number of subjects (e.g., at least 85 subjects for ISO 81060-2) covering a range of blood pressures.

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

• Result from Document: Not applicable. For blood pressure monitors, "ground truth" for individual measurements is typically established by trained human observers using a reference sphygmomanometer (e.g., mercury sphygmomanometer with stethoscope auscultation, or another validated reference device), not by a panel of experts.
• General Context: The "experts" would be the trained personnel performing the reference measurements, following a strict protocol.

4. Adjudication method for the test set:

• Result from Document: Not applicable. Adjudication methods like 2+1 or 3+1 are common in AI/ML studies where human readers provide interpretations. For blood pressure device accuracy, the reference measurements are typically taken by multiple trained observers (sometimes blinded or dual-observer) to minimize individual bias, or by validated automated reference devices.
• General Context: The "adjudication" would refer to how discrepancies between multiple reference measurements are handled, or how the reference measurement is compared to the device's measurement.

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:

• Result from Document: Not applicable. This concept is specific to AI-assisted human interpretation tasks and does not apply to a standalone blood pressure measurement device.

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

• Result from Document: The device, Model CH-4532 Blood Pressure Meter, is inherently a standalone device (an "algorithm only" in the sense that it performs automated measurements without human intervention in the core measurement process). The document states its indication for use is "for oscillometric measurement of systolic and diastolic blood pressure and pulse." The performance of this standalone device would have been evaluated to establish its substantial equivalence. The document, however, does not provide the performance results.

7. The type of ground truth used:

• Result from Document: Not applicable.
• General Context: For blood pressure monitors, the "ground truth" for blood pressure values is typically established by simultaneous or sequential measurements using a validated reference method, such as a mercury sphygmomanometer with a stethoscope (auscultation method) or another validated automated reference device. This is defined by recognized international standards (e.g., ISO 81060-2).

8. The sample size for the training set:

• Result from Document: Not applicable. Blood pressure monitors like this typically do not have a "training set" in the AI/ML sense. Their algorithms (e.g., oscillometric algorithms) are developed based on physiological principles and validated against reference measurements, rather than "trained" on large datasets.

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

• Result from Document: Not applicable. As mentioned above, there isn't a "training set" in the AI/ML context for this type of device.

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