Search Results
Found 1 results
510(k) Data Aggregation
(155 days)
The Disposable Medical 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 proposed device(s) are blue color, and Flat Pleated type mask, utilizing Ear Loops way for wearing, and they all have Nose Piece design for fitting the facemask around the nose. The proposed device(s) are manufactured with three layers, the inner and outer layers are made of spun-bond polypropylene, and the middle layer is made of melt-blown polypropylene filter. The model of proposed device, ear loops, is held in place over the users' mouth and nose by two elastic ear loops welded to the facemask. The elastic ear loops are not made with natural rubber latex. The nose piece contained in the proposed device(s) is in the layers of facemask to allow the user to fit the facemask around their nose, which is made of polyethylene coated steel wire. The proposed device(s) are sold non-sterile and are intended to be single use, disposable device.
The provided text describes the 510(k) premarket notification for a Disposable Medical Surgical Mask (K202511). This is a regulatory submission for a medical device, and as such, the "study" referred to is primarily a set of performance tests conducted to demonstrate substantial equivalence to a predicate device, rather than a clinical trial or an AI algorithm validation study.
Therefore, many of the requested elements regarding AI performance, human reader studies, and sophisticated ground truth establishment are not applicable to this type of medical device submission. The device in question is a physical product (a surgical mask), not an AI algorithm or diagnostic tool.
Here's the breakdown based on the provided document:
Acceptance Criteria and Reported Device Performance
The acceptance criteria are derived from recognized standards for surgical masks, primarily ASTM F2100 levels. The study demonstrates performance against these criteria.
| Item | Acceptance Criteria | Reported Device Performance | Result |
|---|---|---|---|
| Fluid Resistance Performance | 29 out of 32 pass at 160 mmHg | 32 out of 32 pass at 160 mmHg | Pass |
| Particulate Filtration Efficiency | $\ge$ 98% | 98.6% | Pass |
| Bacterial Filtration Efficiency | $\ge$ 98% | 99.9% | Pass |
| Differential Pressure (Delta-P) Test | < 6.0mmH2O/cm² | 3.5 mmH2O/cm² | Pass |
| Flammability Testing | Class 1 | Class 1 | Pass |
Study Details (Applicable to a Medical Device Performance Test)
2. Sample size used for the test set and the data provenance:
- Sample Size:
- For Fluid Resistance Performance (ASTM F1862): 32 samples were tested.
- Data Provenance: The tests were conducted according to established ASTM and MIL-M standards. The country of origin of the data is not explicitly stated beyond the manufacturer's location in China. The data is retrospective in the sense that it's a test of the manufactured product to demonstrate compliance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This is not applicable as this is a performance test for a physical device, not an AI algorithm requiring expert ground truth for image interpretation or diagnosis. The "ground truth" is defined by the objective measurement protocols of the ASTM and MIL standards.
4. Adjudication method for the test set:
- Not applicable for a physical device performance test. Test results are quantitative measurements against predefined thresholds.
5. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:
- No, an MRMC study was not done. This type of study is relevant for diagnostic devices that involve human interpretation (e.g., radiology AI). This submission is for a surgical mask.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Not applicable. This is not an AI algorithm. The performance tests (e.g., filtration efficiency, fluid resistance) are "standalone" in the sense that they are objective measurements of the mask's physical properties.
7. The type of ground truth used:
- The ground truth is based on objective measurement standards and specifications (e.g., ASTM F2100, ASTM F1862, ASTM F2299, ASTM F2101, MIL-M36954C, 16 CFR 1610, ISO 10993-1). For example, for "Fluid Resistance," the ground truth is the pressure at which fluid penetrates the mask when challenged with a synthetic blood spray.
8. The sample size for the training set:
- Not applicable. This is a physical device, not an AI model that requires a training set. The manufacturing process itself could be seen as "training" in a very abstract sense, but not in the context of machine learning.
9. How the ground truth for the training set was established:
- Not applicable. As above, this is not an AI model with a training set and associated ground truth.
Ask a specific question about this device
Page 1 of 1