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MFM-CMS is a software application that is intended for use as a clinical data managing system (also referred to as a clinical information system - CIS).The MFM-CMS Central Monitoring System offers centralized physiological information management of adult, pediatric and neonatal patients which is automatically acquired from multiple bedside monitors. The MFM-CMS provides: collection, display and documentation of data from bedside monitors, viewing of patient physiologic data at remote locations and alarms when the results of the physiologic parameters exceed the user defined limit. It operates with off-the-shelf software. The system is intended for use in a hospital/clinical environment.

The MFM-CMS Central Monitoring System is a software production which runs on PC platform with Microsoft Windows XP or Microsoft Windows 7 operating system. Through specified EDAN protocol, one MFM-CMS can connect with multi-monitor manufactured by EDAN to collect patients' information and monitoring data such as physiological waveforms, physiological parameters and alarms. The MFM-CMS can also send bidirectional control instructions to bedside monitors to change patients' information, alarm limits, and conduct NIBP measurements. The bedside Patient Physiological Monitors have been cleared by FDA under K101539, K120144, K110922, K113623, K113653 and K120173 separately. The monitoring information collect by the MFM-CMS can be saved and printed. At the same time, the old records can be searched conveniently and quickly.

Here's an analysis of the provided text regarding the acceptance criteria and study for the EDAN Instruments MFM-CMS Central Monitoring System:

This device documentation does not contain the level of detail typically found in a study demonstrating performance against specific acceptance criteria for an AI/CADe device. The 510(k) summary focuses more on the software's functionality, its classification, and its substantial equivalence to a predicate device. It lacks quantifiable performance metrics against acceptance criteria.

Therefore, for many of your requested points, the answer will be "Not provided in the text."

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

Explanation: The document describes the system's features and intended use but does not define specific performance acceptance criteria (e.g., accuracy, sensitivity, specificity for particular physiological events like arrhythmia detection, or data display latency). Consequently, no reported device performance metrics against such criteria are provided.

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

• Sample Size for Test Set: Not provided in the text.
• Data Provenance: Not provided in the text.

Explanation: The document mentions "Verification and validation testing was done on MFM-CMS" and lists "Software testing," "Risk analysis," "Safety testing," and "Performance test" as quality assurance measures. However, it does not detail the nature of these "tests," nor does it specify any test datasets, their sizes, or their origin.

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)

• Number of Experts: Not provided in the text.
• Qualifications of Experts: Not provided in the text.

Explanation: Since no detailed performance study or test set is described, there's no mention of experts or their role in establishing ground truth.

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

• Adjudication Method: Not provided in the text.

Explanation: As no test set with expert ground truth is described, there's no mention of an adjudication method.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted or described.
• Effect Size of Human Improvement: Not applicable, as no such study was performed.

Explanation: The MFM-CMS is a central monitoring system for displaying physiological data and alarms from bedside monitors, not a diagnostic AI/CADe device designed to assist human readers in interpreting images or signals more effectively. Its primary function is data aggregation and display. Therefore, an MRMC study comparing human performance with and without AI assistance is not relevant to this type of device and was not conducted.

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

• Standalone Performance Study: Not described in the text in terms of quantifiable metrics.

Explanation: The "Performance test" mentioned is too vague to determine if it constituted a standalone performance study with specific metrics. The device's primary function is to process and display data, not necessarily to perform standalone diagnostic calculations that would have defined standalone accuracy metrics. It is a "software production" that runs on a PC and monitors data, and connectivity/display functionality would be the main "performance" aspects.

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

• Type of Ground Truth: Not provided in the text.

Explanation: Without details on any specific performance tests, the method for establishing ground truth is not described.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable / Not provided in the text.

Explanation: This device is a software application for data collection, display, and alarm management. It is not an AI/Machine Learning device in the sense that it requires a "training set" to learn to perform a diagnostic task. It relies on predefined protocols and inputs from connected monitors.

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

• How Ground Truth for Training Set was Established: Not applicable / Not provided in the text.

Explanation: As it's not an AI/ML device requiring a training set, the concept of establishing ground truth for training is not relevant here.

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