Search Results

The Disposable Nasal Stick-On Mask is intended to provide an interface for application of Respironics BiPAP and CPAP therapy to patients.

The Disposable Nasal Stick-On Mask is intended to provide an interface for application of Respironics BiPAP and CPAP therapy to patients. The mask consists of a faceplate with a contoured skincontacting foam seal. The inlet connector of the faceplate has a pre-assembled, 22mm extendible inlet tube that attaches to a Respironics 22mm circuit. The faceplate has an integrated supplemental exhalation port and strap tabs to accommodate a strap that facilitates holding the mask in place under pressure. The mask is single use, however it can be repositioned on a patient during application and use.

The Stick-On Mask is molded of a soft thermoplastic elastomer, allowing it to flex to fit most adults. The skin-contacting portion is a soft foam material that is bonded to the faceplate of the mask. The foam seal is a laminate of three layers: a skin-contacting layer of a pressure sensitive adhesive material, a center contouring layer made of a soft, mostly closed cell foam, and an interfacing bonding the foam to the faceplate.

The provided text describes a Premarket Notification (510(k)) for a medical device, the "Stick-On Disposable Nasal Mask." The purpose of this submission is to demonstrate substantial equivalence to a legally marketed predicate device.

Key takeaway: This document is for a medical device (a physical nasal mask), not a software or AI-powered device. Therefore, many of the requested criteria, such as "Multi-Reader Multi-Case (MRMC) comparative effectiveness study," "standalone algorithm performance," "ground truth establishment by experts for AI," and "training set sample size for AI," are not applicable.

Here's an analysis of the provided information based on the request, focusing on what is applicable to a physical device submission:

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

The document does not explicitly present acceptance criteria in a table format with corresponding reported performance values for each criterion. Instead, it states that "Testing was performed to demonstrate that the performance of the Stick-On Disposable Nasal Mask in its intended environment is as safe and effective as that of the legally marketed predicate device." and "The Stick-On Disposable Nasal Mask was tested and found compliant with the applicable standards referenced in the 'Draft FDA Reviewer Guidance for Premarket Notifications,' November 1993."

To infer "acceptance criteria," we can look at the general principles of substantial equivalence for a physical device: safety and effectiveness. The "device performance" is implicitly demonstrated through compliance with referenced standards and comparability to the predicate device.

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

The document does not specify the sample size used for performance testing. It generally states "Testing was performed" and "The Stick-On Disposable Nasal Mask was tested."
The data provenance is not mentioned (e.g., country of origin or whether it was retrospective/prospective). This information is typically found in detailed test reports, not usually summarized in a 510(k) summary to this level of detail for a physical device.

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)

This question is not applicable. The device is a physical nasal mask, not an AI or imaging device requiring expert-established ground truth for a test set. The validation would involve physical and mechanical testing, biocompatibility assessments, and potentially human factors testing, but not "ground truth" as understood for AI.

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

This question is not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving discrepancies in expert interpretations, typically in AI/imaging studies. This is not relevant for the performance testing of a physical medical 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

This question is not applicable. MRMC studies evaluate the performance of human readers (e.g., radiologists) with and without AI assistance. The submitted device is a physical nasal mask, not an AI or imaging system.

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

This question is not applicable. The device is a physical nasal mask, not an algorithm.

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

This question is not applicable in the context of "ground truth" for AI/imaging. For a physical device, validation relies on engineering specifications, biocompatibility standards, functional testing, and comparison to the predicate device's established safety and effectiveness.

8. The sample size for the training set

This question is not applicable. The device is a physical nasal mask and does not involve a "training set" in the context of machine learning or AI.

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

This question is not applicable. The device is a physical nasal mask and does not involve a "training set" or "ground truth" in the context of machine learning or AI.

Ask a specific question about this device