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The Medical Gas Analyzer is intended to be connected to other medical devices for monitoring of the breathing gases CO2, N2O and the anesthetic agents Halothane, Enflurane, Isoflurance, Sevoflurance and Desflurane.

It is intended to be connected to a patient breathing circuit for monitoring of inspired/expired gases during anesthesia, recovery and respiratory care. It may be used in the operating suite, intensive care unit and patient room for adult, pediatric and infant patients.The CO2 may also be used in the emergency medical services environment and road ambulances.

Note: The Medical Gas Analyzer shall only be connected to medical devices approved by Prior-care.

The Medical Gas Analyzer is a mainstream respiratory gas analyzer based on infrared gas spectrometry. It is intended to be connected to another medical host device for display of respiratory parameters. It is connected to the patient breathing circuit via the Airway Adapter. This premarket submission adds the C50 Multi-parameter Patient Monitor as a host backboard display to AG200. The C50 Multi-parameter Patient Monitor produced by Shenzhen Comen Medical Instruments Co., Ltd., which has obtained FDA's 510K clearance (K191106).

The concentrations of CO2, N2O, Halothane, Enflurane, Isoflurane, Sevoflurane and Desflurane can be determined together with derived parameters such as waveform data and inspired / expired concentrations of all gases.

The mainstream probe airway adapter is inserted between the endotracheal tube and the breathing circuit, and the gas measurements are obtained through the windows in the sides of the adapter. Running on a standard low voltage DC 5V, the mainstream probe is designed with portability in mind and has low power consumption.

The mainstream gas analyzers are characterized by the following features:

• Low system integration complexity
• Low power consumption
• Fast startup time
• Low weight < 30g
• Visual status indicator

The provided document is a 510(k) clearance letter and summary for the Medical Gas Analyzer (AG200). It does not contain information about a study proving the device meets its acceptance criteria.
The document states: "the subject device does not require clinical test data to support substantial equivalence." This means that the device was cleared based on its similarity to existing devices and bench testing, rather than a clinical study demonstrating its performance against specific acceptance criteria in a real-world setting.

Therefore, I cannot provide the requested information about the study proving the device meets acceptance criteria, the sample sizes, data provenance, expert details, adjudication methods, MRMC study results, standalone performance, or training set details as they are not present in the provided text.

However, I can extract the acceptance criteria as reported in the document through comparison with the predicate device, although these are not explicitly presented as "acceptance criteria" but rather as "device performance" parameters.

1. Table of Acceptance Criteria and Reported Device Performance (as implied by comparison to predicate/reference devices):

2. Sample sized used for the test set and the data provenance:

• Sample Size: Not specified. The document refers to "bench tests" and "software validation testing" but does not provide details on the number of samples or test cases used.
• Data Provenance: Not applicable, as no human clinical or retrospective/prospective data is mentioned. The testing described is "bench testing" and "software validation testing," which are laboratory-based and device-centric.

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

• Not applicable. The document does not describe the establishment of a ground truth by experts for a test set. Testing was primarily technical compliance ("bench tests") and software validation. For the manual anesthetic agent selection, it mentions "qualified clinicians" but no specific number or qualifications are provided, nor is their role in establishing "ground truth" for the device's performance defined.

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

• Not applicable. There is no mention of an adjudication method, as the tests were not clinical studies requiring expert review of medical data.

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. No MRMC comparative effectiveness study was conducted. The device is a Medical Gas Analyzer, not an AI-assisted diagnostic or interpretive tool for human readers.

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

• The testing described, primarily "bench tests" and "software validation testing," assesses the device's performance in a standalone capacity (algorithm only). However, the results are presented as "Pass" for compliance with standards rather than specific performance metrics (e.g., accuracy, precision) in a standalone context beyond what is listed in the performance table. The distinction of "standalone performance" as it typically applies to AI algorithms is not explicitly made, but the listed performance characteristics are inherent to the device itself.

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

• For the bench tests, the ground truth would be established by reference measurement devices or calibrated gas mixtures with known concentrations, as well as adherence to the requirements outlined in standards like ISO 80601-2-55. This is typically a known, controlled input.

8. The sample size for the training set:

• Not applicable. The device is a hardware-based gas analyzer with software; it is not an AI/machine learning device that requires a training set in the conventional sense.

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

• Not applicable. As noted above, the device does not use a "training set" in the context of machine learning.

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