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The CMS-2000 Central Monitoring System provides centralized monitoring and critical care management for patients monitored by bedside monitors. From the CMS-2000, clinicians can gain access to patient information for patients on the Network. The CMS-2000 displays waveforms, parameters and alarm status of bedside monitors for up to 32 patients on a single screen or up to 64 patients using two screens.

The CMS-2000 Central Monitoring System is a software production, which runs on a PC platform running under the Microsoft Windows XP or Windows 7 operating system. Through specified protocol, one CMS-2000 can connect with multi-monitors from ADVANCED INSTRUMENTATIONS to collect patients' information and monitoring data such as physiological waveforms, physiological parameters and alarms. The CMS-2000 can also send bidirectional control instruction to bedside monitors to change patients' information, alarm limits and conduct NIBP measurements. The bedside Patient Physiological Monitors have been cleared by the FDA under K123048 separately. The monitoring information collected by the CMS-2000 can be saved and printed. At the same time, the old records can be searched conveniently and quickly.

The provided text describes a 510(k) premarket notification for the "CMS-2000 Central Monitoring System." This document primarily focuses on establishing substantial equivalence to a legally marketed predicate device (EDAN Central Monitoring System, model MFM-CMS, K120727).

Therefore, the provided document DOES NOT contain the information needed to answer many of the questions regarding acceptance criteria, study design, and performance for a device that relies on novel AI/ML capabilities.

The CMS-2000 is described as a software product that collects and displays physiological data from bedside monitors. It is a Central Monitoring System, which implies it aggregates and visualizes data rather than performing complex analytical or diagnostic functions that would typically require extensive clinical validation studies with ground truth. The submission explicitly states "Clinical testing is not required." and emphasizes "The subject device has similar technology characteristics and has the same intended use, same design principle and same functionality as the predicate device. There are no differences between the devices." This means the FDA cleared the device based on its similarity to a previously cleared product, not on novel performance claims that would necessitate rigorous testing against specific acceptance criteria.

However, based on the information provided, here's what can be inferred or directly stated:

Acceptance Criteria and Device Performance (Limited Information based on the provided text):

As this submission is for a device establishing substantial equivalence to a predicate, the "acceptance criteria" are implicitly met by demonstrating comparable technical characteristics and intended use. There are no performance metrics provided in the format of a typical AI/ML device study.

Detailed Study Information (Based on the provided text, many fields are "Not applicable" or "Not provided" as it's a 510(k) based on substantial equivalence, not a de novo AI/ML device):

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

   • Sample Size: Not applicable/Not provided. The document states "Clinical testing is not required." The evaluation was based on comparison to a predicate device's specifications, not a clinical test set.
   • Data Provenance: Not applicable. No patient data or clinical test set was described.

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

   • Not applicable. No ground truth was established from experts for a test set, as no clinical testing was deemed necessary for this type of device and submission pathway.

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

   • Not applicable. No test set requiring expert adjudication was used.

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. This device is a central monitoring system for displaying already acquired physiological data and alarms from bedside monitors. It is not an AI/ML-driven diagnostic or interpretative tool designed to assist human readers or improve their performance. The submission explicitly highlights that its functionality and intended use are identical to the predicate device.

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

   • Not applicable. The device's "performance" is based on its ability to accurately reflect and transmit data from connected bedside monitors as per its technical specifications, which are compared to the predicate. It does not have a standalone "algorithm" with a specific output requiring a performance study in this context.

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

   • Not applicable. The "ground truth" for this 510(k) submission is effectively the technical specifications and performance claims of the legally marketed predicate device. The CMS-2000 demonstrated its functionality matched the predicate's without requiring independent clinical ground truth.

7. The sample size for the training set:

   • Not applicable. This device is described as a "software production" running on a PC, connecting to monitors. It is not presented as an AI/ML device that requires a training set in the contemporary sense of deep learning or machine learning models. Its functionality is explicitly stated as being "the same" as the predicate based on fixed design principles.

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

   • Not applicable. As no training set for an AI/ML model was described, no ground truth collection for such a set was needed.

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