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The F&P Pilairo Nasal Pillows Mask is intended to be used by individuals who have been diagnosed by a physician as requiring CPAP or Bi-Level Ventilator treatment. The F&P Pilairo Nasal Pillows Mask is intended for single-patient adult use in the home and multiplepatient adult use in the hospital or other clinical setting where proper disinfection of the device can occur between patient uses.

The Pilairo™ is a CPAP nasal pillows mask which has an external connector attached to a frame and face seal that encloses the nasal airway entrance, held in place by a single head strap. The external connector allows connection to a CPAP device. The device conveys pressurised air to the nares. The air may also be humidified and/or oxygen enriched. An oxygen pressure port accessory is available to be used with the device for oxygen therapy and/or gas monitoring.

The provided text is a 510(k) summary for a medical device, the Pilairo™ Nasal Pillows Mask. It details the device's description, intended use, and comparison to predicate devices, focusing on demonstrating substantial equivalence for regulatory approval.

However, the document does not contain the kind of information typically found in a study demonstrating performance based on acceptance criteria for AI/ML-based medical devices. Specifically, it does not include:

• Quantitative acceptance criteria for device performance (e.g., sensitivity, specificity, accuracy thresholds).
• Study design details like sample size for test sets, data provenance, ground truth establishment methods (expert consensus, pathology, etc.).
• Information about human expert involvement (number of experts, qualifications, adjudication methods).
• Details on AI/ML specific studies such as MRMC studies, standalone AI performance, or training set specifics.

The document describes non-clinical tests that were performed to compare the Pilairo™ Nasal Pillows Mask to predicate devices for performance and biocompatibility, stating that these tests demonstrate "substantial equivalence." However, it does not provide the specific results or the acceptance criteria used for these non-clinical tests in a format that would allow for the table requested. The "performance" mentioned refers to engineering performance of the mask (e.g., sealing, pressure delivery), not AI/ML model performance.

Therefore, many of the requested fields cannot be filled from the provided text as they pertain to a different type of device evaluation (AI/ML performance studies) than what is described for this medical mask.

Based on the provided text, here's what can be inferred and what cannot be provided:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states "Copies of test reports are included in Appendix B," but Appendix B is not provided in the input text. Thus, the specific numerical results of these non-clinical tests and the precise quantitative acceptance criteria are not available.

Information Not Found in the Provided Text:

• Any form of AI/ML or algorithm performance metrics (e.g., accuracy, sensitivity, specificity, AUC).
• Quantitative thresholds for these metrics.

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

• Sample Size for Test Set: Not specified. The document mentions "non-clinical tests" but does not detail the sample sizes (e.g., number of masks tested, number of simulated breathing cycles, or simulated patients). This is expected for a physical device rather than an algorithm.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable in the context of AI/ML studies. For this physical device, testing would likely be performed in a lab setting, presumably by Fisher & Paykel Healthcare in New Zealand, as indicated by their address. The nature of testing would be prospective (performing tests on new masks).

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

• Number of Experts: Not specified. This type of human expert ground truth establishment is not typically performed for the evaluation of physical medical masks (like a CPAP mask), as their performance (e.g., seal, pressure delivery) is evaluated through engineering tests and clinical use validation rather than via human expert "reading" of data.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. As there are no human "readers" or interpretations that would require adjudication for this physical device. Performance is measured through engineering parameters.

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

• MRMC Study: No. This type of study is relevant for AI/ML diagnostic or assistive devices where human readers interpret medical images or data. It is not applicable to a CPAP nasal mask.

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

• Standalone Performance: Not applicable. This device is a physical medical mask, not an algorithm.

7. The Type of Ground Truth Used

• Type of Ground Truth: For a physical medical device like a CPAP mask, "ground truth" typically refers to:
  • Engineering Specifications/Standards: Performance is validated against established industry standards or internal engineering specifications for parameters like pressure drop, dead space, leak rates, and material properties.
  • Comparison to Predicate Device: The primary method stated is comparison to the "Fisher & Paykel Healthcare Opus HC482 Direct Nasal Mask for performance" and "Zest™ Nasal Mask for biocompatibility" to demonstrate substantial equivalence. These predicate devices are the "ground truth" standard.
  • Biocompatibility Testing: Adherence to material safety standards.
  • Clinical Use Performance: Implicitly, the mask must function effectively without causing harm during use in patients requiring CPAP/Bi-Level therapy.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a physical product, not an AI/ML algorithm that requires training data.

9. How the Ground Truth for the Training Set was Established

• Ground Truth Establishment for Training Set: Not applicable. As there is no AI/ML algorithm, there is no "training set." The "ground truth" for the device's design and manufacture would be based on human engineering and medical knowledge, design specifications, and successful performance of predicate devices.

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