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510(k) Data Aggregation
(147 days)
The Disposable Medical Masks are intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. They are in infection control practices to reduce the potential exposure to blood and body fluids.
The Disposable Medical Masks are single use, three-layer, flat-folded masks with ear loops and nose clamp. The Disposable Medical Masks are manufactured with three layers, the inner and outer layers are made of non-woven fabric, and the middle layer is made of melt blown Fabric. The ear loops are held in place over the users' mouth and nose by two ear loops welded to the face mask. The ear loops are made of Polyamide & Spandex. The nose clamp in the layers of face mask is to allow the user to fit the face mask around their nose, which is made of galvanized iron wire wrapped with PE material. The Disposable Medical Masks will be provided in white, blue, and purple which are sold non- sterile and are intended to be single use, disposable devices.
This document describes the acceptance criteria and the results of a study demonstrating that the "Disposable Medical Masks" device, manufactured by Hantech Medical Device Co., Ltd., meets these criteria.
1. Table of Acceptance Criteria and Reported Device Performance
| Test Methodology | Purpose | Acceptance Criteria: ASTM F2100 Level 3 | Result |
|---|---|---|---|
| Fluid Resistance, Synthetic Blood Penetration ASTM F1862 | Evaluate Resistance to penetration by synthetic blood (Minimum pressure in mmHg) | ≥ 29 samples out of 32 pass (AQL 4%) at 160 mmHg for level 3 | PASS (32 out of 32 passes at 160 mmHg for level 3) |
| Particulate Filtration Efficiency ASTM F2299 | Evaluate Sub-micron particulate filtration efficiency at 0.1 micron (%) (PFE) | ≥ 98% (29 out of 32 pass) | PASS |
| Bacterial Filtration Efficiency ASTM F2101 | Evaluate Bacterial filtration efficiency (BFE) (%) | ≥ 98% (29 out of 32 pass) | PASS |
| Differential Pressure (Delta P) EN 14683 Annex C | Evaluate Differential pressure (Delta-P) | < 6.0 mmH2O/cm² (29 out of 32 pass) | PASS |
| Flammability, 16 CFR 1610 | Evaluate Flame spread | Class 1 | PASS |
| Cytotoxicity, ISO 10993-5 | Demonstrate biocompatibility safety of the subject device | Non-cytotoxic | PASS |
| Irritation, ISO 10993-10 | Demonstrate biocompatibility safety of the subject device | Non-irritating | PASS |
| Sensitization, ISO 10993-10 | Demonstrate biocompatibility safety of the subject device | Non-sensitizing | PASS |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 3 nonconsecutive lots were used for testing, with 32 samples for each model of surgical mask for the non-clinical tests.
- Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. However, Hantech Medical Device Co., Ltd. is based in the PEOPLE'S REPUBLIC OF CHINA. It can be inferred that the testing was conducted for the purpose of this premarket notification, making it prospective in nature relative to the submission.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
This document describes performance testing for medical masks against established standards. The nature of these tests (e.g., fluid resistance, filtration efficiency, flammability) does not typically involve human expert consensus for "ground truth" establishment in the same way an AI diagnostic algorithm would. The results are obtained through objective physical and biological tests conducted in laboratories. Therefore, information about "experts used to establish ground truth" with specific qualifications is not applicable in this context. The "ground truth" is defined by the technical specifications and criteria within the referenced standards (e.g., ASTM F2100, ISO 10993).
4. Adjudication Method for the Test Set
Given that the tests involve objective measurements against predefined criteria and standards, an adjudication method like "2+1" or "3+1" (typically used for resolving discrepancies in expert interpretations) is not applicable. The results are quantitative and pass/fail based on the physical properties of the masks.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. This type of study is typically performed for diagnostic devices where human readers interpret medical images or data, and their performance with and without AI assistance is evaluated. This submission is for medical masks, which do not involve human diagnostic interpretation.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
No standalone (algorithm-only) performance study was done. The device is a physical medical mask, not an algorithm or AI software.
7. The Type of Ground Truth Used
The "ground truth" for the performance of these medical masks is defined by established international and national standards and their specific criteria. These include:
- ASTM F2100 (Standard Specification for Performance of Materials Used in Medical Face Masks)
- ASTM F1862 (Standard Test Method for Resistance of Medical Face Masks to Penetration by Synthetic Blood)
- EN 14683 (Medical Face Masks - Requirements and Test Methods)
- ASTM F2101 (Standard Test Method for Evaluating the Bacterial Filtration Efficiency)
- ASTM F2299 (Standard test method for determining the initial efficiency of materials used in medical face masks to penetration by particulates)
- 16 CFR 1610 (Standard for the Flammability of clothing textiles)
- ISO 10993-5 (Biological Evaluation of Medical Devices - Tests For In Vitro Cytotoxicity)
- ISO 10993-10 (Biological Evaluation of Medical Devices - Tests for Irritation and Skin Sensitization)
8. The Sample Size for the Training Set
This submission pertains to a physical medical device (disposable medical masks). There is no "training set" in the context of machine learning or AI algorithms for this type of device. The production of such devices involves manufacturing processes that are validated, but not "trained" in the computational sense.
9. How the Ground Truth for the Training Set Was Established
As there is no training set for a physical medical device in the context of an AI/ML algorithm, this question is not applicable. The "ground truth" for manufacturing quality relies on process validation and quality control measures against established product specifications.
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