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This system is intended to monitor physiological parameters of patients within any healthcare environment. The user, responsible to interpret the monitored data made available, will be a professional health care provider. Physiological data, system alarms and patient data analysis will be available to the care provider from the monitor.

The 506CN monitor measures and displays real time physiological data of the patient, including a graphical plethysmogram and numerical data. The 506CN can be used to monitor one or more of the following parameters: Noninvasive BP (NIBP) and SpO2. For all these vital parameters, the 506CN will be capable of limit alarms and alerts, printing of strip chart recordings and storing trends for retrospective review.

The provided text describes a 510(k) premarket notification for the 506CN Patient Monitor. This type of submission relies on demonstrating substantial equivalence to a predicate device rather than conducting new clinical trials to establish acceptance criteria and device performance from scratch. Therefore, the information typically requested in a detailed AI device study report (like sample sizes, expert qualifications, and specific performance metrics vs. acceptance criteria tables) is not present in this document.

However, I can extract the information that is available and explain the context of why certain details are missing for this type of submission.

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

Since this is a 510(k) submission based on substantial equivalence, specific numerical acceptance criteria and a table directly reporting performance against those criteria are not provided in the same way as a de novo clinical study. Instead, the "acceptance criteria" are implied by compliance with recognized standards and equivalence to the predicate device.

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

This information is not provided in the 510(k) summary. For a 510(k) based on substantial equivalence to a predicate device and compliance with standards, detailed clinical test set data (including sample size, provenance, or retrospective/prospective nature) is generally not required to be submitted or summarized. The equivalence is often demonstrated through bench testing, engineering comparisons, and adherence to established performance standards. The document mentions "equivalence testing" but does not detail its methodology or sample size.

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 provided in the 510(k) summary, as it is not a study requiring expert-established ground truth in the typical sense of AI/diagnostic device validation.

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

This information is not provided in the 510(k) summary.

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

An MRMC comparative effectiveness study was not conducted or reported in this 510(k) summary. This device is a patient monitor, not an AI-assisted diagnostic tool, and the focus of the submission is on demonstrating equivalence to an existing monitor, not on improving human reader performance with AI.

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

This device is a patient monitor that displays data for a healthcare professional to interpret ("The user, responsible to interpret the monitored data made available, will be a professional health care provider."). It is not an "algorithm only" device in the AI sense. Its "standalone" performance is assessed by its ability to accurately measure and display physiological parameters, which is affirmed by its compliance with performance standards and equivalence to the predicate device. However, a specific "standalone" study report in the context of AI performance validation is not provided.

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

For this type of device, "ground truth" for NIBP and SpO2 measurements typically refers to reference measurements from established, highly accurate (often invasive) methods against which the device's measurements are validated during standard compliance testing (e.g., AAMI SP-10 for NIBP, ISO 9919 for SpO2). However, the specific methods and sources of this ground truth are not detailed in this 510(k) summary. It only states compliance with the standards that prescribe how such ground truth is established and used for testing.

8. The sample size for the training set

This information is not applicable and not provided. This device is a physiological monitor, not an AI system that undergoes "training" in the machine learning sense. Its functionality is based on established signal processing and measurement algorithms, not learned from a training dataset.

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

This information is not applicable and not provided, as there is no "training set" for this device in the context of AI/machine learning.

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