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The Spacelabs BleaseSirius 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.

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 mechanical ventilator, and may be used for the delivery of anesthetic vapor by use of a dismountable vaporizer. The Spacelabs BleaseSirius is the latest generation product in a family of anesthesia workstations. The same breathing circuits used with the predicate device, the Blease Frontline Sirius 2000, 3000 cleared by FDA in 510(k) premarket notification K051629, are used with the Spacelabs BleaseSirius.

This document describes the 510(k) Premarket Notification for the Spacelabs BleaseSirius Anesthesia Workstation. Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets them:

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

The provided text does not contain a specific table of quantitative acceptance criteria with corresponding performance metrics. Instead, it describes general compliance with predetermined specifications and applicable standards for various types of testing.

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

The document does not specify the sample size used for any of the tests (Electrical Safety, EMC, Performance, or Software). It also does not provide information on the data provenance, such as the country of origin or whether the studies were retrospective or prospective.

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 device is an anesthesia workstation, not an AI or diagnostic imaging device that typically requires expert-established ground truth for its performance evaluation (e.g., in terms of sensitivity/specificity). The compliance is based on engineering and safety standards. Therefore, information about experts establishing ground truth for a test set is not applicable and not present in the document.

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

As the evaluation is based on compliance with predetermined specifications and standards for an anesthesia workstation, an adjudication method for a test set (as typically seen in AI or diagnostic studies) is not applicable and not mentioned in the document.

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 such study was conducted or is mentioned. The device is an anesthesia workstation, 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

This refers to the performance of an algorithm without human intervention. Since the device is an anesthesia workstation and not an AI algorithm, this concept is not applicable. The device's "performance" is its ability to operate according to its specifications and standards, which is assessed through various engineering and safety tests.

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

The "ground truth" for this medical device's safety and performance is established by predetermined specifications and applicable international and national standards (e.g., for electrical safety, EMC, and general performance). This is a technical and engineering-based "ground truth" rather than a clinical one derived from patient outcomes or expert consensus on diagnoses.

8. The sample size for the training set

This device is a hardware/software system, not a machine learning model that requires a "training set." Therefore, this information is not applicable and not provided.

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

As there is no training set for an AI model, this question is not applicable.

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