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The non-prefilled nebulizer is indicated for use when humidity needs to the air flow going to a patient in the form of an aerosol. The large volume nebulizer also allows for the provision of humidified oxygen at select oxygen concentrations from 28% to 98%. This product is for single patient use and is designed to be used in hospital, nursing homes, extended care facilities, outpatient clinics as prescribed by a healthcare professional.

The large volume nebulizer is a non-prefilled reservoir nebulizer for supplying humidity for inhalation therapy. The device features a wing nut style connector that fits standard flow metered medical gas sources and includes a 500mL capacity jar with minimum and maximum fill lines. Large volume nebulize an internal venturi nozzle to draw the solution up from the jar through a small plastic pickup tube and into the gas stream to be aerosolized. A rotating collar sets the delivered oxygen concentration by controlling the size of the room air opening around the venturi. A bull-nose style output connector is used to connect 22mm aerosol tubing for delivery to the patient.

This document is a 510(k) Summary for the Hudson RCI Variable Concentration Large Volume Nebulizer, a medical device. It focuses on demonstrating substantial equivalence to predicate devices rather than proving device performance against specific acceptance criteria for a new and innovative AI-powered product.

Therefore, the requested information categories related to AI/algorithm performance, such as sample size for test sets and training sets, number of experts for ground truth, adjudication methods, MRMC studies, and standalone algorithm performance, are not applicable to this document. This document describes a traditional medical device, not an AI/ML-driven one.

Here's a response tailored to the provided document:

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

The document does not explicitly present a table of acceptance criteria with corresponding reported device performance values in the manner typically seen for novel AI systems with quantitative metrics for accuracy, sensitivity, specificity, etc. Instead, it states that tests were performed to demonstrate safety based on current industry standards and concludes that the device is substantially equivalent to predicate devices. The "performance" here refers to meeting established safety and functional requirements rather than predictive accuracy.

Here's an interpretation based on the provided text, outlining the tests performed and the general "acceptance" that they indicate substantial equivalence:

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

This information is typically found in detailed study reports or clinical trial summaries, which are not part of this 510(k) Summary. The document describes pre-clinical (bench) testing, not human subject data. Therefore, concepts like "test set," "data provenance," "country of origin," and "retrospective/prospective" studies as they apply to clinical data are not present. The "tests were performed" implies an internal engineering and quality assurance process.

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. "Ground truth" in the context of expert consensus is relevant for diagnostic algorithms or subjective assessments. For a physical device like a nebulizer, the "ground truth" is typically established by objective measurements against engineering specifications and validated test methods overseen by qualified engineers and technicians. The document does not specify the number or qualifications of individuals involved in setting or verifying these engineering standards or conducting the tests.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving discrepancies in expert interpretations (e.g., in medical image analysis). For the physical and chemical testing of a nebulizer, the "adjudication" is through meeting predefined analytical standards and specifications, not through expert consensus on subjective findings.

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. MRMC studies are specific to evaluating diagnostic technologies, often AI-driven, where human readers interpret cases. This document is for a physical medical device (nebulizer) and does not involve human "readers" or an AI component.

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

Not applicable. This device is not an algorithm and does not have a "standalone" or "human-in-the-loop" mode for performance evaluation in the context of AI. It is a mechanical device that delivers aerosolized humidity.

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

For this type of device, the "ground truth" is based on:

• Engineering specifications and standards: Performance metrics like oxygen entrainment percentages, humidity output, and mechanical integrity are objectively measured against predefined engineering targets.
• Material compatibility standards: Biocompatibility is assessed against ISO 10993 series standards.
• Safety standards: General device safety is evaluated against recognized industry standards and regulatory requirements.

8. The sample size for the training set

Not applicable. There is no "training set" as this device is not an AI/ML model that undergoes a training process.

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

Not applicable. As there is no AI/ML model, there is no "training set" or ground truth established for it. The standards and specifications are established through medical device development processes and regulatory requirements.

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