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The AS3700 Anesthesia Delivery System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The AS3700 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in both adult and pediatric populations.

The AS3700 Anesthesia Delivery System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The provided text describes a 510(k) summary for the AS3700 Anesthesia Delivery System. This document focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study with specific acceptance criteria and performance metrics for a diagnostic or AI-driven device.

Therefore, many of the requested elements (acceptance criteria table, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, training set details) are not applicable or cannot be extracted from this document.

However, I can extract the information relevant to how the device's performance was evaluated for its 510(k) clearance, which is primarily through compliance with recognized standards.

Here's the closest representation based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The document states that "The AS3700 Anesthesia Delivery System has been tested and found to be in compliance with recognized safety, performance and electromagnetic compatibility standards." This implies a series of engineering verification and validation tests were conducted to ensure the device met the requirements of each listed standard. It also mentions a "risk analysis has been developed to identify potential hazards and document the mitigation of the hazards" and that "The device's software has been verified and validated in accordance with the appropriate test requirements."

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

• Not applicable/Not provided: This is an engineering safety and performance validation, not a clinical study involving human data or a machine learning evaluation. The "test set" would refer to the physical device and its components undergoing testing against engineering standards.

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)

• Not applicable/Not provided: Ground truth in this context refers to the defined specifications within the listed industry standards, which are established by expert consensus within standards bodies, not by individual clinical experts for a specific test set. The engineers conducting the tests would be qualified in medical device testing and regulatory compliance.

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

• Not applicable/Not provided: This pertains to clinical diagnostic studies involving reader interpretations, not engineering compliance testing.

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

• Not applicable: This device is an anesthesia delivery system, not an AI-powered diagnostic or assistive tool.

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

• Not applicable: This device operates with human input (licensed clinicians) and is not a standalone algorithm.

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

• Engineering Standards and Specifications: The "ground truth" for this device's performance is adherence to the technical specifications outlined in the various IEC, ISO, CGA, and ASTM standards listed. These standards represent established best practices and safety requirements for anesthesia delivery systems.

8. The sample size for the training set

• Not applicable: This is not a machine learning or AI device that requires a training set.

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

• Not applicable: This is not a machine learning or AI device that requires a training set.

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