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The A8, A9 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A8, A9 is intended for use by licensed clinicians in the administration of general anesthesia, for patients requiring anesthesia within a health care facility, and can be used in adult, pediatric and neonate populations.

High Flow Nasal Cannula (HFNC) is indicated for delivery of nasal high flow oxygen to spontaneously breathing adult patients. It can be used for pre-oxygenation and short-term supplemental oxygenation (up to 10 minutes) during intubation in operating rooms. It is not intended for apneic ventilation. HFNC is indicated for use in adults only.

The A8, A9 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A8, A9 Anesthesia System incorporates O2, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Halothane, and Sevoflurane). The A8, A9 Anesthesia System is a modified version the previously cleared Mindray A7 Anesthesia System cleared in K171292.

The provided text describes the 510(k) premarket notification for the Mindray A8, A9 Anesthesia System, focusing on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria based on studies involving human readers or AI performance metrics.

Therefore, most of the information requested in your prompt (acceptance criteria table with performance, sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set size, and ground truth establishment for training set) is not available in this document.

The document details engineering tests and conformance to standards, which are different from clinical performance studies for AI/radiology devices.

Here's a breakdown of what is available and what is not:

Information Found in the Document:

• Device Name: A8, A9 Anesthesia System
• Predicate Devices: K171292 (A7 Anesthesia System), K192972 (BeneVision N Series Patient Monitors). Reference devices also listed.
• Technological Differences from Predicate:
  • Change the Vaporizer Type and the addition of Electronic Vaporizers (A9)
  • Change certain parameters of the ventilator modes
  • Addition of the High Flow Nasal Cannula Oxygen (HFNC)
  • Change the Anesthetic Gas Module and Accessories
  • Addition of the Sealed Lead Acid Battery
• Performance Data (Type of Studies Conducted):
  • Functional and System Level Testing (bench testing) to validate performance and ensure specifications are met.
  • Biocompatibility Testing (conformance to ISO standards: 10993-1, -5, -10, -18, 18562-1, -2, -3)
  • Software Verification and Validation Testing (following FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices")
  • Electromagnetic Compatibility and Electrical Safety (conformance to IEC and ANSI/AAMI standards: ES60601-1, IEC 60601-1-6, -1-8, ISO 80601-2-13, -2-55, IEC 60601-1-2)
  • Bench Testing (conformance to ASTM and ISO standards: F1101-90, IEC 60601-1-6, -1-8, ISO 5360, 10079-3, 80601-2-13, -2-55)

Information NOT Found in the Document (and why):

This document is for an Anesthesia System, which is a hardware medical device with integrated software for control and monitoring. It is not an AI-driven image analysis or diagnostic device that would typically involve acceptance criteria related to human reader performance, expert ground truth, or MRMC studies. The "performance data" section focuses on testing the device's functional specifications, safety, and compliance with general medical device standards.

1. A table of acceptance criteria and the reported device performance: Not provided in the format of performance metrics against specific acceptance thresholds for diagnostic accuracy, sensitivity, specificity, etc. The document generally states that "the devices continue to meet specifications and the performance of the device is equivalent to the predicate" based on functional and system-level testing, and compliance with standards. Key technical characteristics are compared in a large table, but this is a comparison to the predicate, not a list of acceptance criteria with measured performance against them.
2. Sample sized used for the test set and the data provenance: Not applicable in the context of this type of device submission. The "test set" here refers to the actual physical devices undergoing bench and functional testing, not a dataset of patient images or clinical cases.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth in this context would be engineering specifications and validated measurement techniques, not expert clinical interpretation.
4. Adjudication method: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No. This type of study is for evaluating diagnostic performance, typically for imaging devices or AI algorithms assisting human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an anesthesia system, not a standalone AI algorithm for diagnosis.
7. The type of ground truth used: For this device, ground truth is established by engineering design specifications, international and national consensus standards (e.g., ISO, IEC, ASTM), and validated measurement instruments.
8. The sample size for the training set: Not applicable for this type of device. There is no "training set" in the machine learning sense described. Software validation ensures the embedded software performs as designed and specified for controlling the anesthesia system.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a regulatory submission for an anesthesia system, which relies on demonstrating safety and efficacy through engineering testing and adherence to established performance standards for medical devices, rather than AI model validation studies common for diagnostic algorithms.

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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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