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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 (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.

This document primarily describes a 510(k) premarket notification for the GE Healthcare Unity Network ID, focusing on its substantial equivalence to a predicate device, Unity Network ID V8 (K170199). It does not contain information about acceptance criteria for device performance with specific metrics or detailed study results where a device's performance is measured against those criteria.

The information provided describes the device's function (data collection and clinical information management), its intended use, and the changes made from the predicate device (primarily software updates to support new third-party devices).

However, it explicitly states:
"The Unity Network ID V9 was tested to assure that the device meets its design specifications. Testing included all new or modified features."
and
"The subject of this premarket submission, Unity Network ID V9, did not require clinical studies to support substantial equivalence."

Therefore, based on the provided text, I cannot describe the acceptance criteria and study as requested, because specific performance acceptance criteria and a study demonstrating the device meets those criteria are not detailed.

The document only states that non-clinical tests were performed to ensure compliance with voluntary standards and design specifications. It lists general quality assurance measures applied during development and testing but does not provide specific performance metrics, sample sizes, ground truth establishment, or expert involvement as typically found in a clinical performance study for AI/machine learning devices.

Here's a breakdown of the specific points you requested, noting what is and isn't available in the provided text:

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

   • Not Available: The document does not provide a table of acceptance criteria nor reported device performance metrics against such criteria. It states the device "meets its design specifications" and "comply with, applicable voluntary standards," but no specifics are given.

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

   • Not Available: No test set sample sizes or data provenance are mentioned as no clinical studies were performed. The testing described is non-clinical verification and validation of design specifications.

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/Not Available: Since no clinical studies were required and no test sets with ground truth are described, there is no information about experts establishing ground truth.

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

   • Not Applicable/Not Available: No clinical test set or adjudication method is described.

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

   • Not Applicable/Not Available: This device is a data collection and management system, not an AI-assisted diagnostic tool. No MRMC study was performed or is relevant for this type of device.

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

   • Not Applicable/Not Available: This device is not an algorithm for diagnostic or prognostic purposes, but rather an interface for data transmission. Standalone performance in the context of an algorithm is not relevant here.

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

   • Not Applicable/Not Available: No ground truth in the context of a clinical performance study is mentioned.

8. The sample size for the training set

   • Not Applicable/Not Available: This device is not an AI/machine learning model that requires a training set in the conventional sense. Its "training" would involve configuring it to correctly interpret and transmit data from specific third-party devices, which is part of its design and verification process, not a machine learning training process.

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

   • Not Applicable/Not Available: See point 8.

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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.

Accessories include device specific interface cables and mounting hardware.

The provided document, a 510(k) Premarket Notification for the Unity Network ID v8, does not contain any information about acceptance criteria or a study proving that an AI/device meets specific performance metrics.

The device, Unity Network ID v8, is described as a data collection and clinical information management system that communicates patient data from various medical devices to a clinical information system. It is explicitly stated that this device is not intended for monitoring purposes, nor is it intended to control any clinical devices.

The document focuses on demonstrating substantial equivalence to a predicate device (Unity Network ID v7). This involves showing that the new version has similar technological characteristics and performance. The changes in v8 primarily involve software updates to support new third-party medical devices.

Therefore, many of the requested elements for describing acceptance criteria and a performance study (especially in the context of AI or advanced device performance) are not applicable to this submission.

Here's a breakdown of why this document doesn't provide the requested information, and what it does provide:

• No AI/Algorithm Component: This device is a data integration and communication system, not an artificial intelligence or an algorithm with diagnostic or prognostic capabilities that would require performance metrics like sensitivity, specificity, or reader studies.
• Focus on Substantial Equivalence: The entire submission is built around demonstrating that Unity Network ID v8 is substantially equivalent to its predecessor. This means focusing on hardware and software changes, and ensuring basic safety and functionality.

However, to address the prompt's structure based on the absence of the requested information:

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

• Acceptance Criteria: No specific numerical performance acceptance criteria (e.g., sensitivity, specificity, accuracy) are stated because the device's function is data collection and management, not diagnostic interpretation. The acceptance criteria would likely revolve around successful data transfer, protocol compatibility, and absence of data loss or corruption.
• Reported Device Performance: No quantitative performance metrics are reported. The "performance data" section in the document refers to compliance with electrical safety, EMC, and software verification/validation standards.

Table:

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

• Not applicable. This device does not use a "test set" of patient data in the sense of a machine learning model. Testing consists of software verification and validation (V&V) through bench tests, ensuring the software correctly performs its intended functions (receiving, converting, and transmitting data) and complies with design inputs. These are functional and non-functional tests of the software itself and its interfaces, not performance on a clinical dataset.

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. There is no "ground truth" in the clinical sense for this device. Its function is data transfer, not interpretation. Software verification does not involve expert clinical review of "ground truth."

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

• Not applicable. No adjudication method is necessary as there's no clinical "ground truth" to establish or interpret.

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

• Not applicable. This is not an AI-assisted diagnostic or interpretive device. No MRMC study was conducted.

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

• Not applicable. This is not an algorithm that produces a clinical output or diagnosis. Its "standalone" function is data integration and transfer, which is addressed through software and hardware verification.

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

• Not applicable. There is no "ground truth" as it pertains to clinical outcomes or diagnoses. The "truth" in testing relates to the correct functioning of the software and hardware according to specifications (e.g., "does data get transferred correctly?", "does the system comply with electrical safety standards?").

8. The sample size for the training set

• Not applicable. This device does not involve machine learning or a "training set."

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

• Not applicable. As there is no training set, this is not relevant.

In summary: The provided 510(k) submission for the Unity Network ID v8 describes a data communication and management device. It does not present performance data or studies typical for AI/ML-driven medical devices that output diagnostic or prognostic information. The "study" mentioned in the document refers to software verification and validation testing, and compliance with general medical device safety and quality standards, rather than clinical performance studies against a "ground truth."

Ask a specific question about this device

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 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 ctinical 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 text describes the Unity Network ID, a physiological patient monitor, and its 510(k) submission. However, it does not contain the specific details required to answer all parts of your request, particularly regarding acceptance criteria, performance data, sample sizes, expert ground truth establishment, or multi-reader multi-case studies.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K071982 Unity Network ID) through a design modification. The "Test Summary" section lists general quality assurance measures applied during development, but not specific acceptance criteria or quantitative performance results.

Here's what can be extracted and what is missing:

Acceptance Criteria and Reported Device Performance

• Risk Analysis
• Requirements Reviews
• Design Reviews
• Subsystem Verification
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing
• Safety testing
• Environmental testing |

Study Details

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

   • Sample Size: Not specified. The document mentions "measurements" and "testing" but does not provide any information on the size of the test set (e.g., number of patients, data points, or test cases).
   • Data Provenance: Not specified. There is no information regarding the country of origin of the data or whether it was retrospective or prospective.

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

   • Not specified. The document does not describe the establishment of a "ground truth" using human experts for the test set. The evaluation seems to be based on engineering and validation testing against internal standards and the predicate device's functionality.

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

   • Not applicable/Not specified. Since there's no mention of expert-based ground truth or performance assessment involving human interpretation, an adjudication method is not described.

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:

   • No. The document makes no mention of an MRMC study. The device is a "Unity Network ID" system that communicates patient data; it is not an AI-assisted diagnostic tool for human readers.

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

   • The device itself appears to be a "standalone" system in the sense that it processes data without direct human-in-the-loop diagnostic interpretation. The document describes it as acquiring, converting, and transmitting data. The "performance testing" and "final acceptance testing" would assess its functional performance in this standalone capacity, but no detailed results for this are provided.

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

   • Not explicitly defined in the context of expert-based ground truth. The "ground truth" for this device's validation appears to be its ability to correctly acquire, convert, and transmit digital data according to specifications and in a manner "as safe, as effective, and performs as well as the predicate device." This would typically be assessed through engineering verification and validation against functional requirements and possibly relevant industry standards, rather than clinical outcomes or expert consensus on medical findings.

7. The sample size for the training set:

   • Not applicable/Not specified. The Unity Network ID primarily acts as a data communication and management system. It's not described as an AI/ML device that requires a "training set" in the conventional sense for learning patterns or making diagnostic predictions. Its development involves "Risk Analysis," "Requirements Reviews," "Design Reviews," etc., which are typical for traditional software and hardware development, not machine learning model training.

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

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

Ask a specific question about this device

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.

Ask a specific question about this device

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 information provided indicates that the Unity Network ID system underwent various quality assurance measures and testing, but it does not specify acceptance criteria in terms of performance metrics or a detailed study demonstrating device performance against such criteria. The document states that the testing "demonstrated that the Unity Network ID is as safe, as effective, and performs as well as the predicate devices," implying a comparison, but lacks the quantitative details usually found in a performance study summary.

Therefore, the following information cannot be fully extracted based on the provided text:

• A table of acceptance criteria and reported device performance.
• Sample size used for the test set and data provenance.
• Number of experts used to establish ground truth and their qualifications.
• Adjudication method for the test set.
• Details of a multi-reader, multi-case (MRMC) comparative effectiveness study, including effect size.
• Details of a standalone performance study.
• The type of ground truth used.
• Sample size for the training set.
• How the ground truth for the training set was established.

Acceptance Criteria and Device Performance:

The document describes the following quality assurance measures:

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

The acceptance criteria are implicitly that the device performs "as well as the predicate devices" in terms of safety and effectiveness, and that it complies with "voluntary standards as detailed in Section 9 of this submission" (though Section 9 is not provided).

The reported device performance is a general statement: "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 quantitative performance metrics are provided.

Other Information:

Due to the lack of specific detail in the provided text, the following cannot be answered directly:

• Sample size used for the test set and the data provenance: Not provided.
• 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 provided.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
• 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 provided. The device described appears to be a data communication system, not an AI-powered diagnostic device that assists human readers.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly detailed, but "Performance testing" and "Final acceptance testing (Validation)" were performed. However, specific results or methodology are not given.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not provided.
• The sample size for the training set: Not provided.
• How the ground truth for the training set was established: Not provided.

Ask a specific question about this device

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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 documentation does not contain information about acceptance criteria, device performance metrics, or a study that evaluates the device's diagnostic performance for medical insights in the way one would typically assess an AI/ML medical device.

The GE Medical Systems Information Technologies Unity Network ID (K021454) is a data communication and management system, not a diagnostic device that generates interpretations or analyses of patient data. Its purpose is to acquire digital data from various medical devices, convert it to a common protocol, and transmit it to other systems like patient monitors, clinical information systems, or central stations.

The "Test Summary" section describes quality assurance measures applied during development, such as risk analysis, requirements reviews, design reviews, and various levels of testing (unit, integration, acceptance, performance, safety, environmental). However, these are developmental tests to ensure the system functions as designed and is safe and effective in its intended role as a data conduit, not to assess its ability to provide clinical insights or make diagnoses.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, standalone performance, type of ground truth, training set information) are not applicable to this device and are not present in the provided submission.

Based on the provided text, here’s a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not applicable / Not provided. The device is a data communication system. The testing described includes unit, integration, and final acceptance testing, as well as performance, safety, and environmental testing. These types of tests typically involve controlled lab environments and specific test cases designed to test system functionality, communication integrity, and adherence to power/environmental standards, rather than a "test set" of patient data in the context of diagnostic performance. There is no mention of patient data being used for device performance evaluation in this context.

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 / Not provided. Ground truth for diagnostic accuracy is not relevant for this type of device.

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

• Not applicable / Not provided.

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

• Not applicable / Not provided. This device is not an AI/ML diagnostic tool; it's a data network interface.

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

• Not applicable / Not provided. The device's function is purely data transmission and conversion; it does not provide an "algorithm only" diagnostic output.

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

• Not applicable / Not provided. The "truth" for this device relates to whether data is accurately acquired, converted, and transmitted without corruption, and whether it functions according to its specifications and regulatory standards. There is no diagnostic ground truth.

8. The sample size for the training set

• Not applicable / Not provided. This device is not an AI/ML model that would require a "training set" of data in that sense.

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

• Not applicable / Not provided.

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