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Siemens FiO2 sensor is indicated for use in an environment where patient care is provided by healthcare professionals, i.e. Physicians, Nurses, Technicians, when the professional determines that the device is required to monitor the oxygen level of ambient air in non-pressurized environments such as that found in infant incubators and oxygen hoods.

The FiO2 Sensor is an addition to the Siemens INFINITY patient monitoring series that measures the oxygen concentration of ambient air in non-pressurized environments such as that found in infant incubators and oxygen hoods. In conjunction with the INFINITY NeoMed pod, the FiO2 Sensor permits oxygen concentration monitoring to be displayed on the INFINITY modular bedside monitors, MultiView WorkStations, and PC's via the INFINITY network.

I am sorry, but the provided text does not contain a study with acceptance criteria and reported device performance for the Siemens FiO2 Sensor.

The document is a 510(k) premarket notification summary, which primarily focuses on demonstrating substantial equivalence to a predicate device. While it lists some device specifications, it does not describe a comprehensive study with specific acceptance criteria, experimental design, or detailed performance results that would allow me to fill in all the requested information.

Here's a breakdown of what can be extracted and what is missing:

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

• Missing from the text: The document does not explicitly state these as "acceptance criteria" for a study, but rather as device specifications that "Meet the requirements of ISO 7767" and are "appropriate for intended use." It doesn't detail a study designed to prove these.

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

• Missing from the text: No information about sample size, test sets, or data provenance is provided. The document mentions "non-clinical performance data" but does not elaborate on a study.

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)

• Missing from the text: Not applicable for this type of device (an oxygen sensor). There's no mention of human experts establishing ground truth, as it's a direct measurement device.

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

• Missing from the text: Not applicable for this type of device.

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

• Missing from the text: Not applicable. This is an oxygen sensor, not an AI-assisted diagnostic tool.

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

• Missing from the text: This device is a standalone sensor. However, the document doesn't describe a formal "standalone study" in the context of an algorithm's performance. It presents the device's technical specifications.

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

• What can be inferred: For an oxygen sensor, the "ground truth" would likely be established through calibrated reference gas mixtures and highly accurate laboratory-grade oxygen analyzers.
• Missing from the text: The document does not explicitly state how ground truth was established for its internal testing.

8. The sample size for the training set

• Missing from the text: This is not an AI/machine learning device, so there's no concept of a "training set."

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

• Missing from the text: Not applicable (no training set).

In summary: The provided document is a regulatory submission demonstrating substantial equivalence based on technical specifications and comparison to a predicate device. It does not contain the detailed study information typically associated with establishing "acceptance criteria" and "device performance" through a formal clinical or large-scale non-clinical study that you might find for more complex diagnostic or AI-driven devices.

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