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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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