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510(k) Data Aggregation
(138 days)
The Disposable Medical Surgical Face 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 Medical Surgical Face Masks are 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 nonwoven fabric, and the middle layer is made of melt blown non-woven fabric. The nose clip is made of PE and iron wire, ear loop is made of Nylon and Spandex. The size of the disposable surgical mask is 17.5*9.5cm with tolerance±1cm, the length of the ear loop is 18cm. 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 describes the acceptance criteria and study results for Disposable Medical Surgical Face Masks (K202211) manufactured by Guangdong Kaidi Garments Co., Ltd. The study focuses on demonstrating substantial equivalence to a predicate device (K153496) through non-clinical testing.
Here's the breakdown as requested:
1. Table of Acceptance Criteria and Reported Device Performance
| Item | Acceptance Criteria (Level 2 Medical Mask) | Reported Device Performance (K202211) | Result |
|---|---|---|---|
| Fluid Resistance (ASTM F1862) | 29 out of 32 Pass at 120 mmHg | 32 out of 32 per lot pass at 120 mmHg, 3 non-consecutive lots tested | PASS |
| Particulate Filtration Efficiency (ASTM F2299) | ≥ 98% | Lot 1: 99.68%; Lot 2: 99.56%; Lot 3: 99.81% (3 non-consecutive lots tested) | PASS |
| Bacterial Filtration Efficiency (ASTM F2101) | ≥ 98% | Lot 1: 99.9%; Lot 2: 99.9%; Lot 3: 99.9% (3 non-consecutive lots tested) | PASS |
| Differential Pressure (Delta P) (EN 14683 Annex C) | < 6.0 mmH2O/cm² | Lot 1: 3.6 mm H2O/cm²; Lot 2: 3.6 mm H2O/cm²; Lot 3: 3.7 mm H2O/cm² (3 non-consecutive lots tested) | PASS |
| Flammability (16 CFR 1610) | Class 1 | Class 1 (3 non-consecutive lots tested) | PASS |
| Cytotoxicity (ISO 10993-5) | Non-Cytotoxic | Non-cytotoxic | PASS |
| Irritation (ISO 10993-10) | Non-Irritating | Non-irritating | PASS |
| Sensitization (ISO 10993-10) | Non-Sensitizing | Non-sensitizing | PASS |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for performance tests: For each performance test (Fluid Resistance, Particulate Filtration Efficiency, Bacterial Filtration Efficiency, Differential Pressure, Flammability), 3 non-consecutive lots were tested, with a sample size of 32 units per lot. This means a total of 96 units were tested for each performance characteristic (3 lots * 32 samples/lot).
- Sample Size for biocompatibility tests: Not explicitly stated but indicated as sufficient to determine cytotoxicity, irritation, and sensitization for the device materials, following ISO 10993 guidelines.
- Data Provenance: The document does not explicitly state the country of origin of the data collection or if it was retrospective or prospective. However, given that Guangdong Kaidi Garments Co., Ltd is based in China, it is highly probable the testing was conducted in China. The data would be considered prospective as it was collected specifically for this 510(k) submission to demonstrate the device's performance against established standards.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
This submission is for a medical mask and relies on non-clinical bench testing against established international and US standards (ASTM, EN, ISO, CFR). Therefore, there were no human experts establishing "ground truth" in the diagnostic sense (e.g., radiologists interpreting images). The "ground truth" is defined by the performance requirements outlined in the referenced standards for a Level 2 medical mask. The tests are objective, laboratory-based measurements.
4. Adjudication Method for the Test Set
Not applicable. As described above, the study involved objective bench testing using standardized protocols for physical and material properties, not human interpretation requiring adjudication.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No. A MRMC comparative effectiveness study was not performed. This type of study is typically relevant for diagnostic imaging AI algorithms to assess the impact of AI assistance on human reader performance. This submission is for a physical medical device (face mask) with performance validated via non-clinical testing.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
Not Applicable. This document pertains to a physical medical device (face mask), not an algorithm or AI software. Therefore, the concept of "standalone performance" of an algorithm is not relevant here.
7. Type of Ground Truth Used
The "ground truth" for the acceptance criteria and performance evaluation is based on established industry and regulatory standards for medical face masks. Specifically, for Level 2 masks, these include:
- ASTM F2100: Standard Specification for Performance of Materials Used In Medical Face Masks
- ASTM F1862: Resistance of Medical Face Masks To Penetration by Synthetic Blood
- ASTM F2101: Bacterial Filtration Efficiency (BFE)
- ASTM F2299: Particulate Penetration Using Latex Spheres
- EN 14683: Medical Face Masks-Requirements and Test Methods (specifically for Differential Pressure)
- 16 CFR 1610: Standard for the Flammability of Clothing Textiles
- ISO 10993-5 and ISO 10993-10: Biological Evaluation of Medical Devices (Cytotoxicity, Irritation, Sensitization)
These standards define the objective performance metrics that the device must meet.
8. Sample Size for the Training Set
Not Applicable. This is a physical device, not an AI/ML algorithm that requires a training set. The term "training set" is typically used in the context of machine learning model development.
9. How the Ground Truth for the Training Set Was Established
Not Applicable. As there is no AI/ML algorithm or training set involved, the concept of establishing ground truth for a training set does not apply.
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