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The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician. The AVEA is indicated for the delivery of air, oxygen or a helium-oxygen combination (Heliox).

The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic breathing gas delivery that provides for neonatal through adult patients. Its module provides maximum flexibility with simple operator interaction. It has a range of user interface interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. The AVEA may be configured as a conventional ventilator or noninvasive positive pressure ventilator (NPPV). It has been designed to function using most commonly available accessories.

The provided text describes the 510(k) summary for the AVEA Ventilator. It focuses on the device's intended use, its substantial equivalence to predicate devices, and a brief description of testing.

Here's an analysis of the available 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 submission does not explicitly provide a table of acceptance criteria with corresponding performance metrics. Instead, it states:

• Same indicated use
• Similar indication as heliox predicate
• Same operating principle
• Same basic ventilator design (except for heliox connector)
• Manufactured and packaged utilizing the same basic processes. |

Missing Information:

• Specific numerical performance requirements for "alarms, controls, and monitors" are not detailed. For example, acceptable ranges for pressure, volume, flow, response times for alarms, accuracy of delivered gas mixes, etc., are not provided.
• The actual measured performance values from the testing are not presented.

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 given text.

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 given text. The device is a ventilator, not an imaging or diagnostic device requiring expert interpretation for ground truth. The "ground truth" (or functional verification) would be against engineering specifications and physical measurements, rather than expert consensus on medical images or diagnoses.

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

This information is not provided in the given text. As this is a ventilator and not a diagnostic device relying on expert interpretation, an adjudication method in the traditional sense (e.g., for medical image reading) would not be applicable. The "adjudication" would be against engineering standards and test pass/fail criteria.

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 comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic devices where human readers interpret medical images or data. The AVEA Ventilator is a treatment device, not a diagnostic one involving human interpretation of AI output.

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

The device itself is a standalone medical device (a ventilator), but it's not an AI algorithm in the context typically discussed for standalone performance studies (e.g., deep learning models making diagnoses). The "performance testing" described involved the device functioning independently, but it's not an AI algorithm in the modern sense. The device does provide "continuous respiratory support," implying its operation without constant direct human intervention once set up, which could be considered "standalone" in its functional capacity.

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

Given the nature of the device (a ventilator), the "ground truth" for performance testing would likely be:

• Engineering specifications and standards: The device's output (e.g., delivered volume, pressure, flow rates, alarm functionality, response times) would be compared against predefined engineering and medical device standards.
• Physical measurements: Using calibrated equipment to measure the actual performance parameters (e.g., gas flow, pressure, oxygen concentration) produced by the ventilator.

The text does not explicitly state the type of ground truth, but implicitly it relates to verifying compliance with performance requirements for "alarms, controls, and monitors."

8. The sample size for the training set

This information is not applicable and not provided. The AVEA Ventilator, described as a "servo-controlled, software-driven ventilator," from 2002, predates the widespread use of machine learning/AI models that would require "training sets" in the modern sense. Its software likely operates based on programmed logic and control algorithms rather than learned patterns from a "training set."

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

This information is not applicable and not provided, as there is no indication of a training set being used in the context of machine learning/AI for this device.

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