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The CNAP® Monitor 500 HD is intended for the non-invasive continuous monitoring of blood pressure, and the determination of associated derived hemodynamic parameters including cardiac output within hospitals. The device displays the blood pressure waveform, trends, and numeric for blood pressure, pulse rate, and associated derived hemodynamic parameters. Alarms are generated for blood pressure parameters and pulse rate. The CNAP® Monitor 500 HD is to be used for adults and is to be operated by healthcare professionals.

The CNAP® Monitor 500 HD is a stand-alone device for continuous non-invasive blood pressure and hemodynamic monitoring with alarming functionality. The continuous non-invasive blood pressure is measured on the patient's finger using a double finger cuff, the oscillometric blood pressure measurement function (Advantage 2.0 OEM module by SunTech Inc.) is used for intermittent calibration of the continuous blood pressure curve. Medium priority alarming can be set for blood pressure beat values and pulse rate.

Based on the provided text, the CNAP® Monitor 500 HD is a blood pressure and hemodynamic monitoring system. The acceptance criteria and the study proving it meets these criteria are primarily focused on its substantial equivalence to predicate devices rather than independent performance metrics against a defined standard.

Here's the breakdown of the information provided, or lack thereof, regarding a detailed AI/algorithm study:

Key Observation: The document describes a medical device, not an AI/ML algorithm. The performance data section focuses on electrical safety, electromagnetic compatibility, mechanical and acoustical testing, and software verification/validation, along with a claim of substantial equivalence to predicate devices based on clinical testing using "measurement data from different sources." There is no indication of an AI/ML algorithm being developed or studied for this device, nor is there a study described that would fit the typical criteria for AI/ML performance evaluation (e.g., sensitivity, specificity, AUC, human reader improvement studies).

Therefore, I will answer the questions based on the closest relevant information provided, while highlighting where the requested details for an AI/ML study are not applicable or not present in the document.

Acceptance Criteria and Device Performance (based on provided text)

The document focuses on demonstrating substantial equivalence to predicate devices. This is the primary "acceptance criterion" from a regulatory perspective. The performance data presented are primarily related to safety, electromagnetic compatibility, and software verification rather than specific diagnostic accuracy metrics for an AI.

Given the nature of the device (a non-invasive blood pressure and hemodynamic monitor), the key performance "acceptance" would be its accuracy in measuring blood pressure and derived hemodynamic parameters, and its safety. However, the document does not provide a table of precise acceptance criteria with numeric targets (e.g., ±5 mmHg for BP accuracy) or how the specific device performance was measured against such targets for the new device. Instead, it relies on demonstrating comparable performance to predicate devices.

Table of Acceptance "Criteria" (based on regulatory submission principles for this device type) and Reported Device "Performance":

Details Specific to AI/ML Studies (Not Applicable or Not Provided)

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

   • The document states "For clinical testing and evaluation measurement data from different sources was used to demonstrate that the device is substantially equivalent to the predicate devices."
   • No specific sample size for a "test set" (as understood in AI/ML validation) is provided.
   • No data provenance (e.g., country of origin, retrospective/prospective) is specified for this "measurement data." This is typical for a traditional medical device submission focused on equivalence, rather than a novel AI algorithm.

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

   • Not applicable / Not provided. The device is a monitor that directly measures/derives physiological parameters, not an diagnostic imaging AI that requires expert labeling for ground truth. Ground truth for blood pressure and hemodynamic parameters in a device like this would typically involve invasive measurements (e.g., arterial line for BP) or established reference methods, not expert consensus on image labels. The document does not describe how this "measurement data" was validated against a gold standard.

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

   • Not applicable / Not provided. This method is relevant for studies involving human interpretation or labeling of data, especially in AI development for image analysis or diagnostics. It does not apply to a physiological monitoring device undergoing substantial equivalence testing.

4. 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 / Not done. An MRMC study is specific to AI-assisted diagnostic tools where human interpretation is part of the workflow. This device is a physiological monitor, not an AI that assists human readers.

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

   • Not applicable / Not explicitly detailed as an AI algorithm study. The device itself is a "standalone" monitor that performs continuous non-invasive blood pressure and hemodynamic monitoring. The "performance data" section broadly covers its functional performance, but not in the context of an isolated AI algorithm. The device's "algorithms" are for signal processing and calculation of physiological parameters, which are validated as part of the overall device functionality, not typically as distinct "standalone AI" studies in the regulatory sense described.

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

   • Not explicitly stated in detail. For a non-invasive blood pressure device, the gold standard (ground truth) is typically invasive arterial blood pressure monitoring. For hemodynamic parameters, it would be direct measurement methods like thermodilution (e.g., using a Swan-Ganz catheter). The document only generically refers to "measurement data from different sources."

7. The sample size for the training set:

   • Not applicable / Not provided. This device is not described as involving a machine learning model that would require a distinct "training set" for its primary function. While there might be internal development data used to optimize signal processing algorithms, it is not presented as an AI/ML training regimen.

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

   • Not applicable / Not provided. As no AI/ML training set is described, this question is not relevant to the provided text.

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