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The Blease Frontline Sirius Range, Anaesthesia Machines are 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 a mechanical ventilator, and may be used for the delivery of anaesthetic vapour by use of a dismountable vaporizer.
The device is intended for use only by a suitably qualified physician.

The Frontline Sirius with 8700 ventilator is similar to the cleared Frontline Sirius anaesthesia machine with the 6200 ventilator. The size and shape of the cabinet are identical, but the pneumatic systems are the same. The ventilator user interface is now graphical with colour LCD and Trak wheel control.

The provided document is a 510(k) summary for a medical device, the Blease Frontline Sirius Range Anaesthesia Machines. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain the specific information required to answer your questions about acceptance criteria and a study proving those criteria are met for the following reasons:

• Focus of 510(k) Summary: A 510(k) summary primarily focuses on demonstrating "substantial equivalence" to a legally marketed predicate device, not on detailing a comprehensive study with acceptance criteria for a new device's performance. It often refers to compliance with standards rather than specific performance metrics.
• Nature of the Device: Anesthesia machines are complex integrated systems. Their "performance" is typically assessed against recognized industry standards (e.g., ISO, IEC) for aspects like gas delivery accuracy, pressure regulation, alarm functions, and safety, rather than through a single "study" with statistical endpoints like those for a diagnostic AI device.

Therefore, many of your specific questions cannot be answered from the provided text. I will indicate where the information is available and where it is not.

Acceptance Criteria and Study for Blease Frontline Sirius Range Anaesthesia Machines

The provided document, a 510(k) summary, indicates compliance with relevant standards rather than presenting a performance study with specific acceptance criteria in the format requested for AI/diagnostic devices. The core of the submission is to demonstrate substantial equivalence to a predicate device (Blease Frontline Sirius with 6200 ventilator) for an updated version (Blease Frontline Sirius with 8700 ventilator).

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

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

This information is not provided in the 510(k) summary. The testing refers to "performance testing," which typically involves bench testing against engineering specifications and relevant standards, not a "test set" in the context of diagnostic or AI studies.

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 information is not provided. "Ground truth" in the context of an anesthesia machine's performance would relate to its ability to accurately deliver gases and maintain patient safety according to established medical and engineering principles, often validated through direct measurement against calibrated instruments, rather than expert consensus on diagnostic images or data.

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

This information is not provided. Adjudication methods are typically relevant for studies involving human interpretation or subjective assessments, which is not the primary mode of validation for an anesthesia machine's core functional performance.

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

There is no indication of an MRMC study being performed. This type of study is completely inappropriate for an anesthesia gas machine, which is a physical device delivering therapeutics, not a diagnostic imaging or AI system that aids human readers.

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

This question is not applicable to this device. An anesthesia machine is not an algorithm; it is a physical and mechanical device with integrated software. Its performance is always "standalone" in the sense that it operates according to its design, delivering gas and monitoring, but it requires a human operator (a "suitably qualified physician" as stated in the intended use) for clinical application and management.

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

The "ground truth" for an anesthesia machine's performance is adherence to established engineering specifications, recognized industry standards (e.g., ISO, IEC for medical electrical equipment, gas delivery systems), and the principles of safe and effective medical gas delivery. This is typically established through:

• Bench testing: Direct measurement of gas flow, pressure, concentration, alarm thresholds, etc., using calibrated reference equipment.
• Compliance with standards: Demonstrating that the device meets the pass/fail criteria defined in relevant national and international medical device standards.

8. The sample size for the training set

This information is not provided and is not applicable to this type of device. "Training set" refers to data used to train machine learning algorithms. The Blease Frontline Sirius Range Anaesthesia Machine is not an AI/ML product in the sense that it would require a training set; it's a conventional medical device.

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

This information is not provided and is not applicable for the reasons stated above.

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