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Use of the KION Anesthesia System is indicated for adult or infant populations in an environment where patient care is provided by Healthcare Professionals, trained in the administration of anesthesia, when the professional determines that a device is required to assist the breathing of the patient. This device can be used to administer anesthesia while controlling the entire ventilation for patients without any ability to breathe, as well as for supporting patients with reduced ability to breathe.

KION is a modular anesthesia system. It is available as a set of hardware and software options, which can be added to take the system from being a low-cost, general purpose anesthesia machine, to a complete state-of-the-art anesthesia workstation. This gives the opportunity to specify a system and then upgrade and modify the system as needs change in the future. The KION anesthesia system is designed to enable the operator to work with the basic components of an anesthesia system in the most suitable way for each patient case. As such, the system is mounted on wheels to allow it to be easily moved into an optimum position, where many of the components can also be individually rotated. The KION system is intended for use on adult and pediatric patients. Physically, the KION consists of three main sections: The Trolley which contains the main pneumatic inlets, power supply and power inlets, battery, communication ports, and the gas delivery system. The Column consists of a revolving Vaporizer Holder, Servo Bellows Absorber and Patient Cassette. The Integrated User Interface consists of the Control Panel and the Monitor. The KION offers the following standard ventilation capabilities: Volume controlled ventilation, Manual ventilation.

The provided text is a 510(k) summary for the KION Anesthesia System, which is a submission to the FDA requesting clearance to market a medical device. This document focuses on demonstrating substantial equivalence to existing legally marketed devices, rather than an in-depth study proving novel acceptance criteria for an AI/ML device. Therefore, much of the requested information about device performance, study design, and ground truth establishment is not typically found in such a document for this type of device.

However, I can extract the relevant information based on the typical content of a 510(k) submission for a non-AI/ML medical device like an anesthesia system.

Here's an analysis of the provided text with respect to your questions:

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

For a device like an Anesthesia System, acceptance criteria are typically related to fundamental performance characteristics (e.g., accuracy of gas delivery, ventilation parameters, alarm functionality) and compliance with international standards. The document primarily establishes equivalence to predicate devices and adherence to relevant standards.

The table below summarizes the key performance parameters mentioned in the "Table of Device Similarities and differences to predicate device" (Section 6) and implicitly considers these as performance benchmarks. However, explicit "acceptance criteria" with specific thresholds are not detailed in this summary, as is common for 510(k) summaries of non-AI devices when claiming equivalence. The "reported device performance" is essentially that the KION system meets or improves upon the performance of the predicate devices.

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 states: "See the test results under section In-house Testion T, in this submission." This indicates that in-house testing was performed. However, the 510(k) summary does not provide details on the sample size used for this testing, nor the data provenance (e.g., country of origin, retrospective/prospective nature). For a device like an anesthesia system, "test set" would typically refer to the various simulations, bench tests, and potentially animal studies, as well as the manufacturing quality control tests conducted during development.

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 in the 510(k) summary. For this type of device, ground truth (or verification/validation reference) would be established through engineering specifications, validated test equipment, and recognized scientific and medical principles for measurement accuracy and safety, rather than through expert consensus in the way it might be for AI/ML imaging devices.

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

This information is not provided in the 510(k) summary. Adjudication methods are typically associated with human review processes, often in clinical studies or for establishing ground truth for AI/ML models. For an anesthesia system, verification and validation would follow engineering testing protocols.

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. A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is specific to diagnostic imaging devices, particularly those involving human interpretation (readers) and AI assistance. This device (KION Anesthesia System) is an anesthesia delivery and monitoring system and does not involve "readers" or "AI assistance" in the diagnostic interpretation sense. Therefore, this type of study was not conducted or applicable.

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

No. This refers to the standalone performance of an algorithm, typically for AI/ML devices. The KION Anesthesia System is a physical medical device with hardware and integrated software for control and monitoring, not a standalone software algorithm in the context of AI/ML.

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

For an anesthesia system, the "ground truth" for performance would be engineering specifications, validated measurement standards and equipment, and established physiological parameters. For example, the accuracy of gas flow would be measured against calibrated flow meters, and alarm thresholds would be set based on clinical safety guidelines and physiological limits. The summary does not explicitly detail these "ground truth" methods but implies them through adherence to standards and in-house testing.

8. The sample size for the training set

This question is not applicable as the KION Anesthesia System is not an AI/ML device that requires a "training set" in the context of machine learning.

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

This question is not applicable as the KION Anesthesia System is not an AI/ML device that requires a "training set" or ground truth for such a set.

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