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The HAMILTON-C3 ventilator is intended to provide positive pressure ventilatory support to adults, pediatrics, infants, and neonates.

Intended areas of use:

• In the intensive care ward or in the recovery room.
• During transfer of ventilated patients within the hospital.

The HAMILTON-C3 ventilator is a medical device intended for use by qualified, trained personnel under the direction of a physician and within the limits of its stated technical specifications.

The HAMILTON-C3 has been designed to ventilate adult and pediatric patients in the critical care environment. With optional support, the HAMILTON-C3 is also able to ventilate infants and neonates. The HAMILTON-C3 ventilator uses the same graphical user interface (GUI) used by the predicate HAMILTON-C2, which features a touchscreen "Ventilation Cockpit". This provides the exact information that the user needs and helps focus on what is important. In addition, the HAMILTON-C3 includes the ASV ventilation-mode, which automatically applies lung-protective strategies, reduces the risk of operator error, and promotes early weaning.
The HAMILTON-C3 has been designed with built-in batteries and a turbine thereby giving the user maximum independence and flexibility to accompany a patient everywhere. The HAMIL-TON-C3 offers the same ventilation modes as the HAMILTON-C2, which provides for both full and partial ventilatory support.

1. The HAMILTON-C3 offers all the conventional modes, as well as advanced invasive and non-invasive ventilation modes: ASV, (S)CMV+, SIMV+, PCV+, SPONT, APRV, DuoPAP, NIV, NIV-ST, nCPAP-PS, PSIMV+, and PSIMV+ with IntelliSync.
2. All 41 monitoring parameters can be trended over 1, 6, 12, 24, and 72 hours.
3. The ability to turn off the Apnea alarm in the nCPAP-PS mode.
4. The HAMILTON-C3 includes a 12.1" wide-screen monitor.

The Hamilton-C3 is a continuous ventilator. The provided text outlines its characteristics, intended use, and a comparison with a predicate device and other FDA-cleared ventilators. However, it does not include detailed acceptance criteria or a study that specifically "proves the device meets the acceptance criteria" in the way one might expect for a diagnostic or AI-driven device with performance metrics like sensitivity, specificity, or F1-score.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving novel performance. The "acceptance criteria" discussed are primarily related to safety, effectiveness, and adherence to recognized standards, rather than specific performance metrics for a novel diagnostic function.

Here's a breakdown based on the information provided, and where information is not present:

1. Table of acceptance criteria and the reported device performance

Based on the document, "acceptance criteria" are inferred from the demonstration of substantial equivalence and adherence to recognized standards. The "reported device performance" is largely presented as meeting the same functional capabilities as the predicate device (HAMILTON-C2) and adhering to various medical device standards.

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 in the traditional sense for this submission type. This is a 510(k) submission based on substantial equivalence, not a clinical trial with a "test set" of patient data to evaluate a diagnostic algorithm's performance. The "testing" referred to is non-clinical verification and validation of the device's functionality and adherence to standards. There is no mention of patient data.

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. As per point 2, there is no "test set" with ground truth in the context of diagnostic performance presented in this document.

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

• Not Applicable. See point 3.

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. This document describes a continuous ventilator, not an AI-assisted diagnostic device. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant or mentioned.

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

• No. This is a medical device (ventilator), not a standalone algorithm. While it contains software, the evaluation in this document focuses on the integrated system's safety and effectiveness and its equivalence to a predicate device, not on the performance of a standalone algorithm or AI.

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

• Not Applicable. As per point 2, there is no "test set" requiring ground truth for diagnostic performance evaluation. The "ground truth" for the device's functionality is its design specifications and compliance with recognized standards.

8. The sample size for the training set

• Not Applicable. This document does not describe the development or evaluation of an AI/ML algorithm that would require a "training set" of data.

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

• Not Applicable. See point 8.

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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 patients 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 is a high-performance 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 is a software-controlled circle system for inhalation anesthesia (Sevoflurane, Desflurane, Isoflurane and/or nitrous oxide). The most important features that enhance FLOW-i performance compared with a traditional anesthesia system are: o a ventilator whose functionality is based on ICU-ventilator technology, the volume reflector technology, o the electronically controlled injector vaporizers and o o the ergonomic design.

The provided text describes a 510(k) premarket notification for the MAQUET FLOW-i Anesthesia System. It outlines the device's description, intended use, and a comparison to predicate devices, but it does not contain information about specific acceptance criteria, study details, or performance metrics in a quantifiable sense typically found in clinical studies or performance validation reports.

Therefore, many of the requested fields cannot be filled from the given text.

Here's an attempt to answer based on the provided text, highlighting what is not available:

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

The document mentions "Performance testing has resulted in data that demonstrates that FLOW-i Anaesthesia System performs within its specifications and within the acceptable limits of the applied performance standards." However, it does not provide specific acceptance criteria values or detailed reported performance data.

2. Sample size used for the test set and the data provenance

The document doesn't explicitly mention "test set" in the context of human studies for performance. The non-clinical testing refers to:

• Sample Size: Not specified.
• Data Provenance: Not applicable as these are non-clinical technical tests, not patient data studies.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. This information would typically be relevant for studies involving human interpretation or clinical evaluations, which are not detailed in this non-clinical testing summary.

4. Adjudication method for the test set

Not applicable. As there's no mention of a human-involved test set requiring ground truth establishment, no adjudication method is described.

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. The FLOW-i Anesthesia System is described as a medical device for administering anesthesia, not an AI-assisted diagnostic or interpretive tool that would involve human "readers" or an MRMC study in this context.

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

The document describes "Non-clinical Testing and Performance" which includes verification of operating data and accuracy of measurements, software validation, electrical and mechanical safety, etc. These are standalone evaluations of the device's components and system without explicit human intervention during the performance measurement itself, fitting a broad interpretation of "standalone performance." However, it's not an "algorithm only" performance in the sense of AI.

7. The type of ground truth used

For the non-clinical testing mentioned:

• Ground Truth: Engineering specifications, regulatory standards, physical measurements, and expected operational parameters. For instance, for "Verification of Operating Data and Accuracy of Measurements," the ground truth would be the known input values and expected output values based on established physics and engineering principles.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device that would typically have a "training set" in the conventional sense. The device's functionality is based on established engineering principles and software logic, not on learning from a dataset.

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

Not applicable, as there is no training set for this type of device.

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