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The Spacelabs Arkon Anesthesia Workstation is intended for use in the hospital environment and operating room. It may be used for the delivery of oxygen, air and nitrous oxide in a controlled manner to various patient breathing circuits with or without the use of mechanical ventilator, and may be used for the delivery of anesthetic vapor by use of a dismountable vaporizer.

The device is intended for use only by a suitably qualified physician.

The Spacelabs Arkon Anesthesia Workstation (Arkon) is an anesthesia workstation that contains all the pneumatic circuitry, controls, monitoring, ancillaries and storage required to control, distribute and mix medical gases and anesthetic agents in order to deliver them to a patient system. It is capable of delivering oxygen, air and nitrous oxide in a controlled manner to various patient breathing circuits with or without the use of a mechanical ventilator, and may be used for the delivery of anesthetic vapor by use of a dismountable vaporizer.

The Spacelabs Arkon Anesthesia Workstation (Arkon) underwent various performance and safety tests to demonstrate substantial equivalence to its predicate device. This submission does not contain information typically associated with AI/ML device performance studies (e.g., sample sizes for test/training, number of experts, specific metrics like sensitivity/specificity for diagnostic tasks). Instead, it focuses on compliance with established medical device standards for hardware and software.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The device performance is reported as compliance with the listed standards and its predetermined specifications. Since this is a traditional medical device submission, the "acceptance criteria" are the successful completion of tests outlined by the referenced standards, and "reported device performance" is the statement that the Arkon complies with these standards.

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

• This is not applicable in the context of this 510(k) summary. The submission describes compliance with engineering and safety standards for a physical medical device, not a data-driven AI/ML system that would typically have a "test set" of patient data. The "tests" here refer to engineering verification and validation activities.

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

• Not applicable for this type of medical device submission. Ground truth, in the AI/ML sense, is not established for this device's performance claims.

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

• Not applicable.

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 was not done. This device is an anesthesia workstation, not an AI-assisted diagnostic or decision-support tool for human readers.

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

• Not applicable. The device is a physical workstation that delivers gases and anesthetic agents, not a standalone algorithm. Its software is integral to its function, and its performance is assessed through compliance with software development and safety standards rather than standalone algorithmic performance metrics.

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

• Not applicable in the traditional sense for AI/ML. The "ground truth" for the Arkon is defined by the technical specifications for its various components (pneumatic circuitry, controls, monitoring, etc.) and the requirements stipulated by the referenced industry and regulatory standards for safety and performance (e.g., electrical safety limits, biocompatibility profiles, ventilator performance parameters).

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device relying on a "training set" of data.

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

• Not applicable.

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