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The indication for FLOW-i Anesthesia System is administering inhalation Anesthesia while controlling the entire ventilation of patients with no ability to breathe, as well as in supporting with a limited ability to breathe. The system is intended for use on neonatal to adult patient populations. The system is intended for use in hospital environments, except MRI environment, by healthcare professionals trained in inhalation Anesthesia administration.

FLOW-i Anesthesia System is a Anesthesia system designed to meet the many ventilatory challenges within Anesthesia, as well as to provide inhalation Anesthesia. It is intended to serve a wide range of patients from neonatal to adult.

FLOW-i Anesthesia System is a software-controlled semi-closed system for inhalation Anesthesia (Sevoflurane, Desflurane, Isoflurane and/or nitrous oxide).

The most important performance features of the FLOW-i Anesthesia System are:

• a ventilator whose functionality is based on ICU-ventilator technology, o
• the volume reflector technology. O
• the electronically controlled injector vaporizers and o
• the ergonomic design. O

This 510(k) submission for the FLOW-i Anesthesia System is based on the following modifications:

• Updates of the product for compliance with 3td edition of the 60601 standard package .
  • IEC 60601-1:2005 O
  • ISO 80601-2-13:2011 o
  • ISO 80601-2-55:2011 O
• Implementation of a new function that provides recommended ventilation values (PBW)
• Possibility to set a lower alarm limit for the Airway pressure alarm: High
• Display of Airway resistance measurement following an Inspiratory and/or Expiratory Hold

This document describes the marketing application for changes to the Maquet FLOW-i Anesthesia System, specifically versions C20, C30, and C40. The primary purpose of the document is to demonstrate "substantial equivalence" to a previously cleared predicate device (FLOW-i Anesthesia System version 3.0, K133958), allowing the manufacturer to market the updated device without a new premarket approval application (PMA).

Based on the provided text, there is no information about a study that proves the device meets specific acceptance criteria in the way a clinical AI/ML device would be evaluated for accuracy or performance on a test set of data.

This document is a 510(k) summary, which focuses on device modifications and demonstration of substantial equivalence through non-clinical testing and comparison to a predicate device, rather than a clinical performance study with predefined acceptance criteria for diagnostic/therapeutic efficacy.

Therefore, it is not possible to fill out the requested table and details regarding acceptance criteria, test set sample size, expert ground truth establishment, MRMC studies, or standalone performance, as these concepts are not applicable to the information provided in this 510(k) submission.

The acceptance criteria here pertain to meeting regulatory standards, ensuring safety, and demonstrating that the modifications do not introduce new questions of safety and effectiveness, meaning its performance is equivalent to the predicate device.

However, I can extract the following relevant information regarding the changes and how the manufacturer demonstrated the device's continued performance and safety:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not present acceptance criteria as quantitative performance metrics for a diagnostic or therapeutic function. Instead, it details that the modifications introduce new functionalities and updates for regulatory compliance, and that the device "performs within its specifications and within the limits of the applied performance standards."

The "acceptance criteria" can be inferred as successful completion of the listed non-clinical tests and demonstration that the modified device remains substantially equivalent to the predicate. The "performance" is the successful outcome of these tests and the comparison to the predicate.

2. Sample Size Used for the Test Set and the Data Provenance:

• Sample Size: Not applicable in the context of a "test set" for a diagnostic/therapeutic AI/ML algorithm. This submission focuses on engineering validation and verification.
• Data Provenance: Not applicable. The tests performed are laboratory/bench testing and engineering evaluations against design requirements and safety standards. There is no patient data involved in the "test set."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts:

• Not applicable. Ground truth in this context refers to engineering specifications and regulatory compliance, not expert clinical labels on patient data. Performance was verified against technical specifications and standard requirements.

4. Adjudication Method (e.g. 2+1, 3+1, none) for the Test Set:

• Not applicable. This is not a clinical study involving human readers or interpretation of results that require adjudication.

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 MRMC comparative effectiveness study was done. This 510(k) is for modifications to a medical device (anesthesia system), not a diagnostic AI/ML algorithm that assists human readers.

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

• Not applicable. The device is a physical anesthesia system with integrated software, not a standalone algorithm. Its "performance" is its ability to deliver and monitor anesthesia according to specifications and safety standards.

7. The Type of Ground Truth Used:

• Ground Truth Type: Engineering specifications, industry standards (e.g., IEC 60601, ISO 80601), and the performance characteristics of the predicate device.
• Examples: Accuracy range for gas concentrations (e.g., Sevoflurane conc: Accuracy: ±0.15 vol% @ (0-1%)), pressure ranges, alarm thresholds, volume measurement accuracy.

8. The Sample Size for the Training Set:

• Not applicable. This is not an AI/ML algorithm trained on a dataset. The software changes are programming implementations, not machine learning model training.

9. How the Ground Truth for the Training Set Was Established:

• Not applicable. There is no concept of a "training set" in the context of this device's modifications. The software and hardware changes are based on design requirements, safety standards, and user needs, which were then verified and validated through non-clinical testing.

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