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AirFit F20 Mask System:

The AirFit F20 mask has two product variants:

• AirFit F20 mask is intended for single-patent reuse in the home environment.
• AirFit F20 SLM (Sleep Lab Mask) variant is intended for multi-patient reuse in the hospital/institutional environment.

Both masks are intended for patients weighing more than 66lb (30kg), who have been prescribed non-invasive CPAP or bi-level positive airway pressure (PAP) therapy. The Sleep Lab Mask is the only variant that is validated and intended for multi-patient reprocessing and must be reprocesssed if reused between patients.

AirFit F20 NM Mask System:

The AirFit F20 Non Magnetic mask has two product variants:

• The AirFit F20 Non Magnetic variant is intended for single-patient reuse in the home environment.
• The AirFit F20 Non Magnetic SLM (Sleep Lab Mask) variant is intended for multi-patient reuse in the hospital/institutional environment.

This mask is for patients weighing more than 66 lb (30 kg), who have been prescribed non-invasive CPAP or bi-level positive airway pressure (PAP) therapy. The Sleep Lab Mask is the only variant that is validated and intended for multi-patient reprocessing and must be reprocessed if reused between patients.

The AirFit F20 and AirFit F20 NM Mask Systems are both full-face masks intended for use with Continuous Positive Airway Pressure (CPAP) and/or Bi-Level devices. CPAP & Bi-Level devices are used for the treatment of Obstructive Sleep Apnea (OSA) and/or ventilatory support. Both treatments deliver pressurized air from a flow generator to the patient. Such delivery of pressurized air relies on the Patient Interface; commonly known as the mask. Full face masks are a common type of Patient Interface (Mask) and are known to efficaciously support the delivery of pressurized air by providing a seal around the patient's nose and mouth.

The AirFit F20 mask system has two product variants:

• AirFit F20 Mask: This is the home use variant that is intended for single patient re-use.
• AirFit F20 SLM (sleep lab mask): This is the variant that is intended for multi-patient re-use and must be reprocessed if reused between patients using the disinfection guide.

AirFit F20 (K170924) had a "For Her" variant which was cleared with aesthetic colour pops, the AirFit F20 Mask and AirFit F20 SLM also have the same "For Her" variant.

The AirFit F20 NM mask system has two product variants:

• AirFit F20 NM Mask: This is the home use variant that is intended for single patient re-use.
• AirFit F20 NM SLM (sleep lab mask): This is the variant that is intended for multi-patient re-use and must be reprocessed if reused between patients using the disinfection guide.

The AirFit F20 and AirFit F20 NM Mask Systems share a common design architecture as the predicate AirFit F20 (K170924), except that the AirFit F20 NM Mask System has non-magnetic clips.

The AirFit F20 and AirFit F20 NM mask systems are made up of the following four main component assemblies: cushion, elbow, frame, and headgear. The cushion and headgear are available in various sizes to allow for adequate mask fit over the intended patient population.

The AirFit F20 and AirFit F20 NM mask systems are prescription devices supplied non-sterile.

The provided FDA 510(k) clearance letter and associated summary pertain to the AirFit F20 Mask Systems, which are non-continuous ventilators (CPAP/Bi-Level masks). This type of device does not typically involve complex diagnostic algorithms or AI components that would necessitate the study structure outlined in your request (e.g., test sets, ground truth, expert adjudication, MRMC studies, standalone performance for AI).

Instead, the clearance is based on demonstrating substantial equivalence to a predicate device (AirFit F20, K170924) through non-clinical performance and safety testing, adherence to recognized standards, and biocompatibility evaluations.

Therefore, many of the specific questions you've asked cannot be directly answered from this document as they are not relevant to the type of device and clearance process involved.

Here's an attempt to extract the relevant information and address your questions where possible:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a "table of acceptance criteria" with corresponding "reported device performance" in a format typically used for diagnostic algorithm validation. Instead, it compares specific design parameters and performance characteristics of the new devices (AirFit F20 Mask System and AirFit F20 NM Mask System) against the predicate device (AirFit F20, K170924). The "Comments" column often indicates that the performance is "Equivalent" or "Identical," implying that the new devices meet or perform similarly to the established safety and performance profile of the predicate, which serves as the de facto acceptance benchmark.

Here's a summary derived from the comparison table (Pages 9-12 of the provided document):

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

The document states that "Non-clinical verification and validation testing completed for the AirFit F20 and AirFit F20 NM Mask Systems demonstrated that the mask systems met all intended performance requirements." This primarily refers to bench testing and in-vitro studies.

• Sample Size: Not explicitly stated as a number of "cases" or "patients" in the context of diagnostic performance. For physical device performance, testing involves multiple units of the product to ensure consistency and reliability, but specific sample sizes for tests (e.g., number of masks tested for exhaust flow, CO2 rebreathing, resistance) are not detailed in this summary.
• Data Provenance: This is not a clinical study in the typical sense. The data comes from non-clinical bench testing conducted by the manufacturer (ResMed Pty Ltd, Australia, with a US office). It is not retrospective or prospective patient data from a particular country, but rather laboratory test results.

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

This question is not applicable. The device is a medical mask system, not a diagnostic algorithm that requires expert interpretation for a "ground truth." The "ground truth" for performance is established by recognized international standards (e.g., ISO 17510:2015 for mask performance, ISO 10993 for biocompatibility) and engineering specifications. Compliance is measured against these standards, not expert consensus on medical images or diagnoses.

4. Adjudication method for the test set

This question is not applicable for the same reasons as #3. Performance is determined by objective measurements against established standards.

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

This question is not applicable. This clearance is for a physical medical device (CPAP/Bi-Level mask), not an AI-powered diagnostic tool. There are no "human readers" or "AI assistance" in the context described for this device.

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

This question is not applicable. This is not an algorithm or AI device.

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

The "ground truth" for this device's performance is based on:

• Compliance with international standards: For physical performance (e.g., CO2 rebreathing, exhaust flow, resistance, AAV pressures, sound) as defined by ISO 17510:2015 and other relevant ISO standards.
• Biocompatibility testing: According to ISO 10993 series and ISO 18562 series.
• Mechanical integrity testing: To ensure durability after various environmental stressors (home cleaning, transport, storage, operation, free fall, reprocessing).
• Cleaning and reprocessing validation: Against standards like ISO 17664-1, ISO 17664-2, ST98, and AAMI TIR12.

Essentially, the ground truth is objective measurement against established engineering and safety standards.

8. The sample size for the training set

This question is not applicable. There is no "training set" as this is not a machine learning or AI algorithm.

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

This question is not applicable for the same reasons as #8.

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