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The Unity Network ID is indicated for use in data collection and clinical information management through networks with independent bedside devices. The Unity Network ID is not intended for monitoring purposes, nor is the Unity Network ID intended to control any of the clinical devices (independent bedside devices/ information systems) it is connected to.

The Unity Network ID system communicates patient data from sources other than GE Medical Systems Information Technologies, Inc. equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies Inc. patient monitors.

The Unity Network ID acquires digital data from eight serial ports, converts the data to Unity Network protocols, and transmits the data over the monitoring network to a Unity Network device such as a patient monitor, clinical information system or central station.

The provided document is a 510(k) summary for the GE Healthcare Unity Network ID V7. It describes a data collection and clinical information management system.

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list specific quantitative acceptance criteria (e.g., accuracy, sensitivity, specificity) for the Unity Network ID V7, nor does it present device performance against such. Instead, it focuses on demonstrating that the device meets design specifications and complies with applicable voluntary standards.

The "Determination of Substantial Equivalence: Summary of Non-Clinical Tests" section indicates: "The Unity Network ID V7 and its applications were tested to, and comply with, applicable voluntary standards. The Unity Network ID V7 was tested to assure that the device meets its design specifications."

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in the context of clinical or performance data from a specific dataset of patients or cases. The testing described is primarily software and hardware verification and validation, rather than an evaluation against a clinical dataset. Therefore, there is no mention of sample size or data provenance (country of origin, retrospective/prospective).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

Since there is no "test set" based on patient data, there is no mention of experts needed to establish ground truth or their qualifications. The "ground truth" in this context refers to the successful functionality and compliance of the device against its specifications and standards.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a test set requiring adjudication by experts.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and Effect Size

No, an MRMC comparative effectiveness study was not done. The device is a data collection and management system, not an interpretive diagnostic tool that involves human readers interpreting clinical output. The document explicitly states: "The subject of this premarket submission, Unity Network ID V7, did not require clinical studies to support substantial equivalence."

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

The "testing included all new or modified features" and involved various quality assurance measures like unit-level testing, integration testing, performance testing, and safety testing. These tests would evaluate the algorithm's functionality and accuracy in its intended role of data conversion and transmission. So, in essence, the "standalone" performance of the data conversion and routing algorithms was assessed as part of these non-clinical tests. However, it's not a "standalone performance study" in the typical sense of evaluating diagnostic accuracy.

7. The Type of Ground Truth Used

The "ground truth" for the non-clinical tests appears to be:

• Design specifications: The device's output and functionality were compared against predefined technical and functional specifications.
• Voluntary standards: Compliance with relevant engineering and medical device standards (though specific standards are not listed in this summary, they are implied).
• Expected behavior: For simulated use testing and verification, the "ground truth" would be the expected correct data conversion and transmission as per the device's design and independent bedside device protocols.

8. The Sample Size for the Training Set

Not applicable. This device is a data integration and conversion system, not an AI/ML model that requires a "training set" in the typical sense of machine learning for image analysis or diagnostics. Its functionality is based on established communication protocols and data mapping.

9. How the Ground Truth for the Training Set was Established

Not applicable, as there is no training set for this type of device.

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The EV1000 Platform is indicated for use primarily for critical care patients in which the balance between cardiac function, fluid status and vascular resistance needs constant or intermittent assessment. The EV1000 Platform may be used in all settings in which critical care is provided.

The EV1000 Platform consists of two components: a databox and a monitor. The databox is where all incoming signals are processed. It contains all the algorithms for parameter calculation. It is mounted to the patient bedside or to an IV pole. The databox has mounts so that pressure transducers, FloTrac sensors, and the VolumeView System can be attached to it. The databox has no graphical user interface. It connects, via an Ethernet cable, to the EV1000 monitor.

The EV1000 monitor is a panel PC with a touchscreen interface. It is connected to the databox by an Ethernet cable. It is intended to be mounted to an IV pole. The monitor does not process any data; its sole function is to act as a user and communication interface.

The EV1000 Platform, when connected to the VolumeView system, intermittently measures or calculates intermittent cardiac output, intermittent cardiac index. , intermittent stroke volume. intermittent stroke volume index, systemic vascular resistance, and systemic vascular resistance index.

When connected to a FloTrac sensor, the platform continuously measures or calculates arterial pressure cardiac output, stroke volume, stroke volume index, stroke volume variation, systemic vascular resistance, and systemic vascular resistance index.

The provided FDA 510(k) summary for the EV1000 Platform focuses on its substantial equivalence to predicate devices based on functional and safety testing. It does not contain the kind of detailed information typically found in acceptance criteria and efficacy studies for AI/algorithm-based diagnostic devices, particularly regarding quantitative performance metrics, sample sizes for test/training sets, expert ground truth adjudication, or comparative effectiveness studies with human readers.

Here's why and what can be inferred:

Key Takeaway: The EV1000 Platform is a hardware monitoring system that calculates physiological parameters using algorithms. It is not an AI/ML diagnostic device in the modern sense that would typically have the requested data (e.g., diagnostic accuracy, sensitivity, specificity, or reader studies for image interpretation).

The document states: "It contains all the algorithms for parameter calculation." This implies that the algorithms perform calculations based on physiological signals, rather than interpreting complex data like medical images or making a diagnosis. Therefore, the "acceptance criteria" would likely be around the accuracy of these calculations compared to a known standard or the predicate devices, and functional safety.

Based on the provided text, here's what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

Detailed Breakdown of Other Requested Information:

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: Not specified in the provided text.
• Data Provenance: Not specified. Given the nature of a medical device submission, it would likely involve clinical data, but its origin (e.g., country, specific hospitals) and whether it was retrospective or prospective are not mentioned.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:

• Number of Experts: Not applicable/not specified. For this type of device (physiological parameter calculation), "ground truth" would be established by reference methods or validated sensors, not by expert interpretation of data. If there were a need for expert review of device output for clinical acceptability, it is not mentioned.
• Qualifications: Not applicable/not specified.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable/not specified. Adjudication methods (like 2+1, 3+1) are typically used when multiple human readers interpret complex data, such as medical images, to establish a consensus ground truth. Here, output parameters are calculated numbers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

• MRMC Study: No, it was not done. MRMC studies are specifically designed to assess the diagnostic efficacy of a system (often AI-assisted) by comparing multiple human readers' performance with and without the system's help on multiple cases. This device is a physiological monitoring platform, not an image interpretation or diagnostic aid in that context.
• Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was conducted.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done:

• Standalone Study: The summary implies that the "functional testing" and "comparative analysis" against predicate devices would assess the algorithms within the system directly. However, the details of how this was done (e.g., comparison of calculated parameters against a gold standard or a reference device on a dataset) are not provided. It simply states the device "has been demonstrated to be as safe and effective."

7. The Type of Ground Truth Used:

• Ground Truth: For a device calculating physiological parameters, the ground truth would typically be established by:
  • Reference Methods: Such as thermodilution for cardiac output, or direct arterial line measurements for arterial pressure, using independently validated instruments.
  • Predicate Device Comparison: Performance relative to the legally marketed predicate devices would be a primary comparison point for demonstrating substantial equivalence.
  • The specific type of ground truth used to validate the accuracy of the calculated parameters is not detailed in this 510(k) summary.

8. The Sample Size for the Training Set:

• Training Set Sample Size: Not specified. This device calculates parameters using algorithms, meaning it's likely based on established physiological models and signal processing rather than machine learning algorithms that require a "training set" in the common sense (i.e., for learning from annotated data). If machine learning was used, the training set size would be crucial, but it's not mentioned.

9. How the Ground Truth for the Training Set Was Established:

• Ground Truth for Training Set: Not applicable/not specified. As above, this device's algorithms are likely model-based, not learned from a large annotated training set. If there were development data, its ground truth establishment is not described.

In summary: The provided 510(k) summary for the EV1000 Platform is for a physiological monitoring device that calculates parameters. The information it contains aligns with demonstrating "substantial equivalence" based on functional and safety testing compared to predicate devices, rather than the detailed performance metrics and study designs (like MRMC or reader studies) typically associated with modern AI/ML diagnostic tools focused on pattern recognition or complex data interpretation. The summary lacks the granularity for the acceptance criteria and study details that would be present for an AI product.

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