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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 equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies 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.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Unity Network ID device:

The provided text does not contain acceptance criteria presented as quantitative metrics or specific performance thresholds for the Unity Network ID. Instead, it describes general quality assurance measures and states a conclusion that the device performs as well as its predicate. Therefore, much of the requested information cannot be extracted directly from this document.

Here's what can be provided based on the input:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample size for the test set: Not specified in the provided summary.
• Data provenance: Not specified in the provided summary (e.g., country of origin, retrospective or prospective).

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

• Not applicable/Not specified. The document describes testing related to system verification and validation, but not expert-based ground truth establishment for clinical performance.

4. Adjudication method for the test set

• Not applicable/Not specified. The testing described (Risk Analysis, Requirements Reviews, Design Reviews, Subsystem Verification, Integration testing, Final acceptance testing, Performance testing, Safety testing, Environmental testing) does not indicate a need for a clinical 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

• No. An MRMC comparative effectiveness study is not mentioned. The device, Unity Network ID, is a data communication system and not an AI-assisted diagnostic tool for human readers.

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

• Yes, implicitly. The listed "Performance testing," "Safety testing," and "Environmental testing," along with "Subsystem Verification," "Integration testing (System verification)," and "Final acceptance testing (Validation)" would constitute standalone testing of the device's functionality and performance as a data communication system. The device itself does not involve a human in the loop for its core function of data transmission.

7. The type of ground truth used

• For the technical and performance testing of a data communication system like Unity Network ID, the "ground truth" would be established by:
  • Functional Specifications/Requirements: The device is expected to correctly acquire, convert, and transmit data according to its design specifications.
  • Industry Standards: Compliance with relevant voluntary standards (as mentioned in "Section 4.2 Specific Standards and Guidance").
  • Predicate Device Performance: The underlying assumption and stated conclusion is that the device performs "as well as the predicate device," implying a comparative baseline for performance.
  • Objective Measurements: Performance, safety, and environmental testing would rely on objective measurements against predefined criteria (though these criteria are not detailed in this summary).

8. The sample size for the training set

• Not applicable. The Unity Network ID describes a data communication hardware/software system, not a machine learning or AI algorithm that requires a training set in the typical sense.

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

• Not applicable, as there is no mention of a training set for an AI/ML algorithm.

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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 equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies 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 text describes the Unity Network ID, a device designed for data collection and clinical information management. The information mainly focuses on its regulatory submission and general testing, rather than a specific study proving its performance against acceptance criteria in the context of an AI/human reader evaluation.

Therefore, many of the requested categories regarding acceptance criteria and a specific study cannot be fully answered from the provided document.

Here's a breakdown of what can be extracted and what cannot:

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

• Requirements Reviews
• Design Reviews
• Testing on unit level (Module verification)
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing
• Safety testing
• Environmental testing |

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

• Not explicitly stated for a specific "test set" related to performance metrics. The document describes general "testing on unit level," "integration testing," and "final acceptance testing," but no details on sample size or data provenance for these tests are provided.

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)

• Not applicable. The device is a "physiological patient monitor" communication system, not one that requires expert interpretation for a "ground truth" like medical imaging or diagnostics. The validation seems to be against engineering and regulatory standards.

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

• Not applicable. No adjudication method is mentioned as there's no indication of interpretation by multiple experts.

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

• No. This is not an AI-based device, nor is there any mention of a human-reader study.

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

• Partially. The device itself is "standalone" in its function as a data communication system. Its performance evaluation would be based on its ability to accurately acquire, convert, and transmit data, rather than an "algorithm only" performance in the context of diagnostics or interpretation. The document mentions unit-level, integration, and final acceptance testing, which imply standalone functional evaluation.

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

• Not explicitly stated in terms of a "ground truth" for diagnostic purposes. For this type of device, "ground truth" would likely refer to the correct functioning of data communication protocols and accurate data transfer, which would be measured against engineering specifications and validated data streams, rather than medical ground truth like pathology.

8. The sample size for the training set

• Not applicable. This device does not appear to use a "training set" in the context of machine learning.

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

• Not applicable. This device does not appear to use a "training set" in the context of machine learning.

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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 equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies 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 text describes a medical device called "Unity Network ID," which is a device for data collection and clinical information management through networks with independent bedside devices. It is not a device that involves AI (Artificial Intelligence) or machine learning algorithms for diagnostic or prognostic purposes, and therefore, many of the requested categories related to algorithm performance, training data, ground truth, and expert evaluation are not applicable.

The document discusses compliance with voluntary standards and quality assurance measures for the development of the system. The "Test Summary" indicates that the Unity Network ID was subjected to various testing phases to ensure its safety and effectiveness.

Here's an attempt to answer the questions based on the available information, noting when information is not present or not applicable to this type of device:

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

• Requirements Reviews conducted
• Design Reviews conducted
• Unit level testing (Module verification)
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing conducted
• Safety testing conducted
• Environmental testing conducted | All listed quality assurance measures were "applied to the development of the system." | This indicates process adherence rather than specific quantitative performance metrics. The results of these measures "demonstrated that the Unity Network ID is as safe, as effective, and performs as well as the predicate devices." |
  | Functional Equivalence | As safe, as effective, and performs as well as the predicate device (K021524 Unity Network ID). | "The results of these measurements demonstrated that the Unity Network ID is as safe, as effective, and performs as well as the predicate devices." | No specific performance metrics (e.g., data transfer rates, error rates) are provided to quantify "as well as." |

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

The document describes testing phases (unit, integration, final acceptance, performance, safety, environmental testing) for the device's functionality and safety, not for an AI algorithm's diagnostic or predictive performance on a patient data set. Therefore, there is no mention of a "test set" in the context of patient data, sample size, or data provenance (country, retrospective/prospective) for evaluating an AI's performance. The testing would have involved the device itself and its interaction with other systems.

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 is not applicable as the device is a data collection and management system, not an AI diagnostic tool that requires ground truth established by medical experts on patient data.

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

This is not applicable for the same reason as point 3.

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 study is not applicable because the Unity Network ID is a data infrastructure device, not an AI system that assists human readers in interpreting medical cases.

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

This is not applicable as there is no AI algorithm being evaluated for standalone performance. The device itself is a standalone hardware/software system designed for data management.

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

This is not applicable. The "ground truth" for this device would relate to its ability to correctly acquire, convert, and transmit digital data as per its specifications, rather than clinical outcomes or expert consensus on medical conditions. The "ground truth" would be defined by the expected behavior and data integrity within the network.

8. The sample size for the training set

This is not applicable. The device does not involve machine learning; therefore, there is no "training set" in the context of AI model development.

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

This is not applicable for the same reason as point 8.

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