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510(k) Data Aggregation
(154 days)
The Disposable Surgical Masks are intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. These face masks are intended for use in infection control practices to reduce the potential exposure to blood and body fluids. This is a single use, disposable device(s), provided non-sterile.
The Disposable Surgical Mask is composed of mask body, nose clip and ear loop. The body of the mask is composed of three layers: the inner and outer layers are made of Spun-bond polypropylene, and the middle layer is made of melt blown non-woven fabric, the nose clip is made of plastic materials and iron wire, ear loop is made of spandex.
The size of the disposable surgical mask is 17.5*9.5cm with tolerance±5% cm, the length of the ear loop is 16cm, and the length of the nose clip should no less than 8.0cm.
The outer layer of disposable surgical mask will be provided in blue, the inner layer of the disposable surgical mask will be provided in white, and it will be provided with non-sterile and is intended to be single use, disposable devices.
The provided document is a 510(k) summary for a Disposable Surgical Mask (K201893). This type of regulatory submission demonstrates substantial equivalence to a predicate device based on non-clinical performance data. It does not involve AI or complex medical imaging, and therefore, many of the requested criteria (like ground truth establishment by experts, MRMC studies, training set details, or sample size for test sets in an AI context) are not applicable to this document.
However, I can extract and present the relevant "acceptance criteria" and "device performance" in a structured way, based on the provided tables.
Acceptance Criteria and Reported Device Performance for Disposable Surgical Mask (K201893)
The study proves the device meets the acceptance criteria through a series of non-clinical performance tests designed to show equivalence to a predicate device (K182514).
1. Table of Acceptance Criteria and Reported Device Performance
| Item | Acceptance Criteria (for Level 2 Surgical Mask) | Reported Device Performance (Subject Device) | Result |
|---|---|---|---|
| Fluid Resistance (ASTM F1862) | ≥ 29 out of 32 pass at 120 mmHg | 32 out of 32 pass at 120 mmHg | Pass |
| Particulate Filtration Efficiency (ASTM F2299) | ≥ 98% | 99.62% | Pass |
| Bacterial Filtration Efficiency (ASTM F2101) | ≥ 98% | 99.9% | Pass |
| Differential Pressure (Delta P) (EN 14683 Annex C) | < 6.0 mmH2O/cm² | 5.2 mmH2O/cm² | Pass |
| Flammability (16 CFR 1610) | Class 1 | Class 1 | Pass |
| Biocompatibility: Cytotoxicity (ISO 10993-5) | Non-cytotoxic | Non-cytotoxic | Pass |
| Biocompatibility: Irritation (ISO 10993-10) | Non-irritating | Non-irritating | Pass |
| Biocompatibility: Sensitization (ISO 10993-10) | Non-sensitizing | Non-sensitizing | Pass |
Explanation of Applicability for Other Requested Information:
The following points are not applicable to this 510(k) submission for a non-AI medical device (surgical mask):
- 2. Sample size used for the test set and the data provenance: For physical performance tests like fluid resistance, specific sample sizes are often defined by the testing standards (e.g., 32 samples for ASTM F1862). The tests are typically conducted in a laboratory setting, and the data provenance would be internal testing reports, not patient data from specific countries or retrospective/prospective studies.
- 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This is for AI/imaging devices where human interpretation is the ground truth. Not applicable here.
- 4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as there's no human interpretation or ground truth adjudication in the context of physical performance testing 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: Not applicable. MRMC studies are for AI-assisted diagnostic devices.
- 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This refers to AI algorithm performance.
- 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" here is the defined performance standard (e.g., "≥ 98% BFE"). It's based on established scientific and engineering principles for material performance, not clinical outcomes or expert consensus on medical images.
- 8. The sample size for the training set: There is no "training set" for physical performance testing of a disposable surgical mask. This concept applies to machine learning models.
- 9. How the ground truth for the training set was established: Not applicable, as there is no training set.
In summary, this document describes a traditional medical device submission where the acceptance criteria are based on established performance standards for the product type, and the study proves compliance through direct physical and material testing, not through AI model validation methodologies.
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