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510(k) Data Aggregation
(203 days)
to act as physical barrier including fluidresistant properties, provide only very low impediment to breathing, avoid fogging of surgeons glasses, effective filtration of particles, droplets, not hinder speech. These are the same as those of the predicate devices These products also have the same intended yses as similar products currently cleared for marketing by the 510(k) process
These disposable surgical masks are formed of three layers and are flat-fold disposable face masks comprised of two arcuale segments which form a comfortable mask covering the nose and mouth areas of the face The outer mask surface is of non-woven polypropylene The middle layer, between the non-woven outer and inner layers, is made of "Web Dynamics" air filtration media. This material is designed as a highly efficient bacterial filter with very low pressure drop, to faciliate breathing and minimize spectacle fogging. The facial layer is of non-woven polypropylene. The mask is designed to be resistant to penetration even when body fluids strike it with impact, while allowing cool, comfortable breathing
The provided text describes a 510(k) premarket notification for a new surgical mask. The information focuses on establishing substantial equivalence to predicate devices rather than proving a device meets specific acceptance criteria through a study with predefined performance metrics. Therefore, many of the requested sections (e.g., sample sizes for test and training sets, expert qualifications, MRMC studies) are not applicable to this type of regulatory submission.
However, I can extract the 'acceptance criteria' implicitly from the tests performed to demonstrate substantial equivalence, and report the 'device performance' as stated in the submission.
Here's a breakdown of the requested information based on the provided document:
Acceptance Criteria and Device Performance
| Acceptance Criteria (Implied from Tests) | Reported Device Performance |
|---|---|
| Resistance to penetration by synthetic blood | Passed (Protocol 9617002-01) |
| High Bacterial Filtration Efficiency (BFE) | Demonstrated |
| Low differential pressure (Delta P) for breathability | Demonstrated |
| No Primary Dermal Irritation | Passed |
| Low Particulate Shedding | Passed |
| Acceptable Flammability | Passed |
| No Cytotoxicity | Passed |
| Same intended use as predicate devices: act as physical barrier including fluid-resistant properties, provide very low impediment to breathing, avoid fogging of surgeons glasses, effective filtration of particles/droplets, not hinder speech. | Substantially equivalent to predicate devices with these characteristics. |
| Technological characteristics same as predicate devices. | Substantially equivalent to predicate devices. |
| Materials substantially equivalent to predicate devices. | Substantially equivalent to predicate devices. |
Study Details (Based on the 510(k) submission type)
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
- Sample Size: Not specified in the document for the performance tests. Implied to be sufficient for the specific test protocols.
- Data Provenance: Not explicitly stated, but the tests were performed as part of a U.S. regulatory submission (Hauppauge, NY is mentioned as the submitter's location). The studies are inherently prospective, as they were conducted to support the 510(k) submission for this specific 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 is not applicable. The tests performed are laboratory-based and do not involve expert interpretation or clinical ground truth establishment in the way, for example, a medical imaging AI product would.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- This is not applicable. Adjudication methods are relevant for studies involving human interpretation or clinical endpoints, not for laboratory performance tests of a surgical mask.
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 is not applicable. MRMC studies are typically used for medical imaging or diagnostic devices where human readers interpret results, and the device assists or provides a diagnosis. This submission is for a physical barrier device (surgical mask).
6. If a standalone (i.e., algorithm only without human-in-the loop performance) was done
- This is not applicable. There is no "algorithm" in the context of a surgical mask. The performance tests are for the physical properties of the mask itself.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- The "ground truth" for the performance tests is based on established laboratory standards and protocols for evaluating surgical masks (e.g., synthetic blood penetration, bacterial filtration efficiency). It's not clinical "ground truth" derived from patient data.
8. The sample size for the training set
- This is not applicable. There is no training set in the context of physical property testing for a surgical mask. This is not a machine learning or AI device.
9. How the ground truth for the training set was established
- This is not applicable, as there is no training set.
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